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We’ve constantly been sharing a list of our recent reads in our weekly emails for The Good Investors.

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But since our readership-audience for The Good Investors is wider than our subscriber base, we think sharing the reading list regularly on the blog itself can benefit even more people. The articles we share touch on a wide range of topics, including investing, business, and the world in general.

Here are the articles for the week ending 10 July 2022:

1. Kenneth Stanley – Greatness Without Goals – Patrick O’Shaughnessy and Kenneth Stanley

[00:08:44] Patrick: In the book and in the presentation you gave last week, there’s a key central example that, like you said, you stumbled upon via some of your own research. I would like to walk through that story. I want to just plant the key idea before we do that with another quote from the book, which is that, “Almost no prerequisite to any major invention was invented with that invention in mind.” You used that term stepping stones, the things that we combine. You gave the example of vacuum tubes and computers. People working on vacuum tubes weren’t thinking about computers, and there’s a million examples like this. So I just want to plant that idea out there. The stepping stones thing not resembling the final invention is the reason why it can’t be so deterministic, and here’s our objective, set up the steps between now and there. Maybe you can start to introduce that concept via the Picbreeder example that I think was the way that you originally alighted upon this idea in your research.

[00:09:31] Ken: It’s neat because, in a way, this is a story of serendipity, which is about serendipity. I mean, basically, this pic breeder just serendipitously led to this insight. Picbreeder was an experiment that I was running with my lab. I was a professor at the time at the University of Central Florida, where we allowed people on the internet to go and breed pictures. I know this is a major digression from what we were just discussing. We were discussing all these important things and we’re talking about breeding pictures. So how do these things connect? Breeding pictures, it is a little esoteric from general societal concerns perspective, but it’s basically about searching through a space in a way. This was an opportunity for us when we were doing artificial intelligence research to crowdsource. Crowdsourcing is really interesting. Let’s say you to take people on the internet because you’ve got access to potentially thousands, millions of people and have them try to do something collectively. Wouldn’t have been possible in the past if you didn’t have access to the internet. What we wanted to do was to crowdsource people, to search through the space of images or pictures and what that meant. So we used breeding. So basically, what it meant was that you could take an image, say a blob or something, and in fact, the site would start you off with random blobs if you started from scratch and you could say, “Look at some blobs and you could pick the one you like the best,” just like you might if you were breeding horses or dogs, “Pick the one you like the best.” You might have different reasons or criteria, but whatever your criteria is it’s fine, and then it would have children.

So it’s a little strange. It sounds strange. The picture has children, but this is inside of a computer. So if you think about it, why not? The picture can have children. The children or the offspring of the picture are like any other children. They look like it. They’re not exact duplicates just like if you have children, they look a little bit like you. They’re not exact duplicates of you or your spouse either. That’s the case here. So then what’s cool is that then you can see that if your picture that you chose has children, then you can look at the children and then you can pick from those children which one you like the best. You can see that this is in effect breeding. So then out of those, you pick your favorite there. It has children, and then you get to choose from those, and then so on and so forth. You’re basically iterating generations of breeding, where it goes depends on what you choose up to you. To tie this back quickly, what does this have to do with anything? If you think about those images, they’re basically a metaphor for discovery in general. If you think about like what you said about vacuum tubes and computers, computers are a discovery, vacuum tubes are a stepping stone on the road to that discovery. So somebody chose to use those vacuum tubes to try to build a computer. When it comes to image breeding, if I see an image that looks like something interesting, and then I choose to breed it further and then I get something else, maybe a picture of a skull, which actually was discovered, then I basically used that stepping stone to get to a discovery. So somehow, there’s a metaphor, an analogous metaphor here.

What’s cool about this site, what made it, I think, compelling to me was that because it’s crowdsourced, what we allowed people to do was to come in and look at what other people had bred. So there’s this big database and it’s being displayed in a natural way, a way that makes it easy for people to see what’s been discovered to surface things that are interesting. So those you can think of as stepping stones. You might see a butterfly or a face or something like that. Then someone who sees that is allowed to instead of starting from scratch, instead of starting from blobs like you would if you were starting from scratch, they can start from your discovery. If you found a butterfly and somebody wants to breed new butterflies, then no problem. They don’t have to start from scratch and get to a butterfly. They can start from your butterfly and then breed from there. It’s called branching. So that means that people are building off of the discoveries of their predecessors or you could think of as standing on the shoulders of their predecessors, which is, again, it’s a really nice analogy, I think, to how human innovation proceeds in general, where someone invents something, discovers something, comes up with an idea, and then someone else that they might not even know later in the future goes back in history and sees that thing and realizes this could be used for that, and it transfers that idea over and it becomes a stepping stone to something else. This has been going on for as long as civilization, basically is civilization. That’s what basically causes civilization to happen. So pic breeders are a microcosm of that, but here’s where the thing that leads to the insight that’s profound and to me was shocking was that after running this site for a couple years, so this is a long time, and letting people just breed and discover things and they discovered all kinds of things, butterflies and cars and planets.

[00:14:01] Patrick: We’ll put a link in and a collection to some of these. It’s really staggering, the things that you see that started with black blobs.

[00:14:07] Ken: Yeah. Yeah. So you’ll get a chance to see it. They found all this stuff after a couple years of watching this. Then what we found was that underneath the hood, we were able to look at how. If you think about just for a second, just think about why Picbreeder is fascinating. At first, it might seem like a toy or something. What is it actually for? People are playing around and breeding images, which have no purpose other than just that they’re images, but actually, what is, I think, profound about having something like that is that it is basically a history of discovery in all of its minute detail. Every little thing that everybody decided to do throughout the history is recorded. We don’t have artifacts like that. We don’t know every step of every invention that’s ever been made. A lot of it just happened inside of someone’s head. So this is not recorded, but Picbreeder is one of the few things, maybe the only thing where every single step of everything is recorded completely. So that meant that after a couple years, we could go back and find out what actually explains how everything was discovered, and I turned out to be, I think, shocking. The shocking revelation was that in almost every single case, more than 99% of cases, if you looked at something interesting, like a car, for example, or a butterfly or a bird or whatever it might be, if you go back in its history and you look at what were the steps that led to that thing, the steps look nothing like it at some point back. Right before you get to it, it might look like it, but if you go back far enough, you will find a stepping stone that looks absolutely nothing like it in 99.9% of cases.

Why is that a revelation? Well, the problem is that if you think about it, what that means is that the only way to discover any of these things was to not be trying to discover them. Now, usually if you say things like that, that sounds like some new age statements, discover things by not trying to discover, and that’s mystical or something. Now, think about this. I’m not talking in the new age perspective. This is an empirical observation. This is actually what happened. The people who discovered these things who are responsible for the stepping stones that led to the discoveries were not actually trying to discover those things because if they had been, then they wouldn’t have chosen the things when they had their selections. They had these blobs they could look at. They could choose one of them. They wouldn’t have chosen the ones they chose if they were trying to get the final product. For example, you have a case where there was an alien face that led to a car. Who would choose an alien face if they want a car? That would not be a good idea, but what happened was the wheels of the car, which was depicted from the side, actually derived from the eyes of the alien face. Again and again and again, you see this phenomenon that in hindsight, you can see what happened, but looking forward, you would never imagine that these connections could be made. This shows, in fact, it’s true in Picbreeder that you can only find things in the long run by not looking for them. You need to take your eyes off the ball in order to be able to accept the stepping stones that ultimately make finding the ball possible, which I think is totally contrary to our culture, to our way of making discovery, the way we think things should be done, which is always objectively driven. So the connection that I need to make, I think, beyond that is to justify why I would extend from that discovery to real life.

[00:17:21] Patrick: If you think about the power of these images, most of them were achieved across what I’ll call a modest amount of generations. We’ll talk about AI and machine learning a little bit later on, which is so interesting because almost all of it has an objective function. It’s almost all objective-based. So that’ll be an interesting part of our conversation, but when you put up the number of generations of breeding to get from a blob to a clear bird, let’s say, it was only 80, 90, 40, 100. It wasn’t that many iterations. Then you showed us a skull, a picture of a skull, and really drove the point home by describing, “Okay. Now, let’s imagine this specific skull or one very close to it is our objective.” Could we get close to it across way, way, way more generations and actually targeting it? Maybe you can describe that experience because I found that to be a powerful nail in the coffin.

[00:18:10] Ken: So basically, we took this and we said, “Let’s try to drive the point home and also just see if we can validate this hypothesis that you can only find things by not looking for them by actually looking for them explicitly.” Just to make it fun, I think this twist makes it fun, let’s look for things that we already saw were discovered. That makes this crazy because it’s like we know that these can be discovered in this space. Like you said, I think it is an important point that these things were not discovered with a lot of compute, so to speak. If I recall, I think it’s 72 generations, might be 74, 72, 74 steps or iterations. That is just ridiculously low. When you think about it in terms of compute, of course, these are humans making these selection steps, but in machine learning, modern machine learning, it’s pretty reasonable to have millions of iterations to get to something meaningful. Here, we’re talking about dozens. In some way, that says these are easy. These are not hard discoveries. In some sense, they’re still impressive because of the fact if I just randomly choose blobs in blob space, in the space of the Picbreeder, you’ll never find anything. 99.99999% of the space is just garbage blobs. So these are still needles in haystack, but what’s weird is that the needles in the haystack are discoverable within a few dozen steps. One conclusion you might draw naively would be that, “Oh, they can’t be that hard to find.” The skull is let’s say 74 steps trivial, basically, from a compute perspective. So let’s set up an experiment and see. So what we can do is we can say, “Let’s get an image matching algorithm,” which are available, which basically tells me if I show this algorithm a blob, I input this blob and I ask it to compare it to the skull, it’ll tell me how far away we are, how close is this image to a skull.

That comparison will help me because when I show a bunch of blobs, I can just have it automatically pick the one that’s closest to the skull. It’s really simple. Then every iteration can be done now by the computer instead of by a human. So we can automate it. Good old fashioned machine learning here. We just automate Picbreeder. No more humans in the loop, and we’ll just automate it to go to the skull. I think to me, this sounds like a worthy adversary. I would be worried this might actually work. It shouldn’t work though our hypothesis is correct because our hypothesis here is that you can only find things by not looking for them. Now, this is explicitly looking for the skull. This is a metaphor for how we do things in our culture. So we say, “This is our goal. This is our OKR. This is what we’re going to achieve this quarter, and now we’re going to work towards it. You’re going to give me a metric. In this case, it’s skull matching. Let’s match the skull picture,” and then you’re going to cut off branches that don’t seem to be maximizing that metric and go by the branches that do seem to be maximizing the metric and just move towards the skull. We’re going to do that now explicitly. We gave it 30,000 steps. This takes about 74 steps, let’s say, for the first discovery by a human. Now, we’re giving an automated algorithm, 30,000 steps, just for fun, just in case, I don’t know, it needs extra time. We’ll give it way extra time, orders of magnitude. What happens? Failure every single time. We ran this dozens of times. It’s every single time failure.

It’s also fun to look at the failures because you can see it’s trying. You see it shadows. It’s like somebody stumbling, almost getting there but not quite. Well, it’s not even close, but it’s like getting the silhouette shadow of what it wants, but it can’t get even close. It’s just fascinating. That’s much more compute. It should be able to eventually overcome it, but the thing is that it highlights the reason that this is happening in if you look at it. Why are all discoveries happening this way in Picbreeder? It’s actually because the world is deceptive, which means that the things that lead to skulls don’t look like skulls. This is the fundamental insight, which is not being recognized across society. It’s that the things that lead to the things you want don’t look like the things that you want. There’s actually a name for this in philosophy. It’s called the like causes like fallacy. I think it’s from Mills. We all seem to assume. It seems to be almost like built in to us biologically that the things that lead to what we want are going to resemble what we want. I don’t know why we all believe this, but it’s not how the world works. If you think about it, that makes total sense. If the world actually worked that way, if the like causes like fallacy was actually true, actually things do resemble where you want to go, we would solve all that problems…

…[00:24:01] Patrick: There’s one piece of this that I love that hasn’t been mentioned yet, which is the role of the individual and their decisions relative to I’ll call this heterogeneous decision making versus homogeneous ruling by committee or something or making choice by committee. Talk about the importance of the individual and their choice in this web of invention and disruption.

[00:24:20] Ken: Yeah. This is a funny thing. It’s true. This is another very popular mythology, I think, in our culture is let’s get together and collaborate, bring all the smart people into the room. It’s not just like, “Let’s get interdisciplinary collaboration. Let’s get the computer scientists sitting there with the economist.” All these things are very exciting to us. I just want to say I’m not saying we shouldn’t have collaboration. That again would be this crazy cranky thing to say. What I do want to get to what you’re asking about is that collaboration itself also is subject to a number of caveats because of the insight about the paradox, the objective paradox, and that means there’s a right way and a wrong way to think about collaboration. It’s quite dangerous. We tend to do it the wrong way. The issue that comes up here is that if you look at Picbreeder, I think something that’s very intriguing about what happens in it is that once somebody sees a stepping stone on the site, so if you recall, like I said, all the discoveries that other people had made are made available for you. So what it means is you are seeing a history of stepping stones when you go to this site. You don’t have to start from scratch. If somebody found a butterfly, you can start from their butterfly.

When you come in and see that butterfly, that is a point of collaboration. It’s implicit collaboration, but it is collaboration because somebody else did work, they found the butterfly, and now you’re building off of that work. So collaboration is happening. However, the moment you choose to continue or what we call branch from the butterfly to breed it further, you are on your own. This is a very unique thing. At first, it sounds like, “Oh, well, what’s the big deal? You’re on your own, okay,” but think about it. We almost never allow people to do that in collaborative situations in our culture. We always bring people together and move towards consensus almost immediately, but in Picbreeder, it’s not like that. Instead, you choose the thing you think is interesting and it was your choice and nobody else was involved in that choice. Now, think about this compared to, for example, I was a professor for a long time. So I think a lot about asking for grants, science grants. That’s like picking an image. It’s like what project do I want to pursue. You come in and you see a butterfly and you want to pursue the butterfly. It’s like you’re sending a grant proposal to the NSF. You think something interesting will happen if you choose this butterfly, but the thing about the NSF is now it’s going to go to a committee. I am not allowed to just go off on my own and work on that butterfly now. There’s going to be a committee that thinks about the decision that I’m making, and I have to justify usually objectively in the sense that I’m going to have to say where it’s going to lead.

What are you going to get by doing this butterfly? That is not how Picbreeder is. You are on your own completely, and not only are you on your own by choosing the butterfly, you’re on your own every single step of the way until you publish the thing you discovered. So there’s no interference, whatsoever, and you’re just on your own. Think about the difference between that and the way we run things where it’s basically you come into a room with all these people, you bring up these ideas, you have this discussion, you try to come to consensus. All the crazy things you would’ve done are basically cut off at the start by this surge towards consensus, which is going to lead to what I would call convergent consensus because we’re trying to move toward convergence very quickly. What you’d understand from Picbreeder is the proliferation of the stepping stones that gives the power to the process. The reason that I can get to cars, there was a discovery of a car which came from an alien face, was because of the discovery of the alien face. No one would ever think that you needed an alien face to get to a car, but the alien face is there not because somebody was thinking about cars, but because there is a general culture inside of Picbreeder of proliferating stepping stones.

This is not generally how we run collaborative systems because we run them by consensus, which is the exact opposite. That’s about pruning out stepping stones. People start generating things and then we start saying, “No, no, no. Committee doesn’t like this. Committee doesn’t like that.” We then converge to the thing, which is basically the consensus basis of current thinking, which tends to be dogmatic and tends to be status quo and everything that we basically want to get away from, and then all these radical stepping stones, which are the interesting things which could lead to places we’re not expecting for the very reason that the things that we’d want to get to don’t look like them so we need the radical stepping stones are the things that we cut out. You can see from this theory or philosophy way of looking things that a lot of the way we run collaborative systems is just totally kneecap at the start, and also should, I think, be rethought.

[00:28:31] Patrick: Can you describe when you put a consensus mechanism into this experiment, the outcome falling to all this? I promise we’re going to get to some of the bigger implications here in a minute, but this simplified example is this so damn powerful for how we all are going to spend our time in our lives. Maybe just describe the outcome when you insert consensus mechanism into how these generations progress.

[00:28:54] Ken: This is super interesting, and it’s funny because it’s just a coincidence that this happened because there was another project that was launched around the time of Picbreeder called the living image project. It had nothing to do with me other than it used basically the same in coding under the hood as Picbreeder. This is nice because it creates a controlled experiment by accident because both Picbreeder and this other thing, the living image project, have this underlying coding that’s the same. So what that means is in principle, they can achieve the same thing. They could find similarly cool stuff in principle, but there’s this one difference, which makes this very interesting as a comparison, which is this living image project did work by consensus. I mean, the reason it did is because I think it’s because there’s this cultural assumption just like riding on top of that. They’re like, “This is a good way to do things. Let’s have a vote.”

So basically what they said is, “Okay. Here’s what we’re going to do. Just like Picbreeder, there’s these blobs, they’re arranged on the screen, you can see all these blobs, and we’re going to pick one of them. That’ll be the parent of the next generation of blobs.” However, the difference from Picbreeder is that the choice will be made by a vote. So over the course of a week, people will come in and it would turn out basically hundreds of people would come in, and they would vote on their favorite blob and then we’ll choose the one that gets the most votes. To a lot of people, this is really intuitive. More opinions are better than one. Let’s use the crowd to decide what to do, but consistently with what I just argued, the result are starkly different and terrible in comparison. I don’t mean to cast any dispersion on the living image project. I think it was a cool idea to try it. It really helps to illustrate. The problem here is that you get a washout effect. Imagine you come in, okay? There’s hundreds of people coming in. Imagine you like butterflies and I like cars. Now, what’s going to happen when we vote and we’re just looking at blobs? The blobs don’t look like butterflies yet and they don’t look like cars, and you want a butterfly and I want a car. What is going to happen? Complete washout is what’s going to happen.

There’s no way you’re going to get enough people on your side. You don’t even know. We don’t even know what each other are doing or have understanding of how you even get these things. So what’s going to happen is you get this mildly aesthetic blobby pattern type of consensus. We get the mildly, most pleasing blob aesthetic, and then that’s going to happen at every iteration because there’s another few hundred people voting at the next iteration and another few hundred, and after thousands and thousands of, I think it was 25,000 votes, you can look at the top ranking, all you have are amorphous rainbowy blobs every single thing. I think it’s just stark and shocking. Even though it’s in this totally obscure genre of stuff like breeding pictures, I think it should give us all heart palpitations because we’re running our culture this way…

...[01:08:29] Patrick: It’s incredible, and I think demands one last question. This idea that you’ve referenced over and over again that no one is telling people how they have to behave in something like pick breeder. There’s a permissionless nature to it. There’s a individuality and individual interpretation of events. With all that in mind, for those whether it’s running a grant organization or running a labs, an AI labs or innovation labs inside of a company or anyone that has resources like Ed did that want to deploy those resources in service of disruption and innovation, either generative or protecting against it or whatever, you’ve already talked about what they do wrong. If you were in-charge of one of those, an allocator of resources to create innovation, how would you do it?

[01:09:14] Ken: I think if you’re in a position like that, you’re a gatekeeper. So you are responsible for the perpetuation or not of this objective culture. It’s especially relevant if you’re purportedly involved in fostering innovation because that’s where this gatekeeper has a huge influence. Yeah. I would recommend doing things differently. You probably exist in their framework where that’s very difficult because you answer to somebody. They don’t understand where you suddenly say, “Well, I’m not assessing things in this normal objective way anymore.” They’re like, “What the heck are you doing? How do we know this is working?” So this takes some courage, I think. The first thing I would say, get the courage because there’s nothing we can do about that. You have to explain to them, “If we’re not going to follow the usual security blanket rooted things, the people in the chain are going to have to be convinced and that’s hard work.” That’s why I think it’s worth having a conversation like this show. That’s why we wrote the book. It’s like we wanted to start people having these conversations. So get the courage to have the conversations and really fight because it’s not going to happen if you don’t. You’re just going to shut down. You’re going to think, “I want to do this, but, eh. On the other hand, my boss wants this. His boss wants that. There’s a funding agency out there or we have investors.” You’re like, “Forget it. It’s too complicated.” Somehow you got to fight this.

Now, in terms of actually practical implementation, what should you do? What I would say is you should be maximizing stepping stones in the pursuit of innovation, not maximizing an objective performance. There’s two things, maximizing stepping stones and maximizing exposure to stepping stones. The thing that makes innovation work is that the people who could run with something are exposed to the thing that they could run with, and that is what’s missing I think from a lot of these organizations is that we have these filters, which are extremely narrow, which decide what comes through, and they end up pruning out things. It’s the conversion consensus problem. Things don’t get exposed to a person who would react dramatically if they were exposed to that thing. What we should do is greatly broaden the filters that go from idea to exposure to the people who could run with the ideas and then also change the criteria for what should be pursued. You have to recognize that if you pursue something that requires investment, so it costs money. So we’re not talking about decisions that can be made lightly. Nobody can say, “Well, everything will pursue because now we’re all going to be open-minded. We’re just going to do everything everybody wants.” That cannot happen. Some things have to not happen, but the way that we decide what happens, I think the criteria should be quite different.

It should not be trying to move to consensus, get a committee to agree with something, get the most vote, something like that. It should be many people within the context of the organization, whatever many means. Many people are exposed to the ideas that are being generated, and that basically only one or two need to trigger the success of that idea or to say, “This is worth investing,” but then you say, “Well, how can that be?” Then every idea would have to be invested because somebody might want to invest in everything. The reason I think it can make sense is if there’s skin in the game for the people who are validating the ideas. If I see something that is so exciting to me that I’m personally willing to pursue it that I didn’t come up with myself, just like the alien face that led to the car in Picbreeder, then I’m actually willing to spend my time on what you did. I’m actually giving something away. I could have had that time. I could have invested in something else. What should make the confirmation of something meaningful and really worth investment is if the person who’s confirming it is giving something away. Maybe they lose their right for some period of time to have their idea even considered or they give away the resources that they were giving for some project that they had. There’s obviously finite resources, but if someone’s willing to do that, that means that this thing means a lot to them, and it only takes one person, magic connection, electric connection to happen, and we have to somehow create those connections. It’s not going to be consensus matter. It’s going to be a niche thing. When there’s something incredible, it’s not going to be tons of people see, it’s going to be one out of a hundred see it, and that has to be honored somehow. We have to find a way to do that.

2. Conversation at Panmure House – Howard Marks and Patrick Schotanus

PS: In fairness to Russell, it was in my introduction to Russell’s question [i.e., not in Russell’s question itself] that I said the economy is mechanical and that’s the definition of mainstream economics.  Russell and I do not necessarily agree on that.  But to continue on mechanical economics as a theory: In your memo On the Couch, you talk about your own early exposure to the efficient-market-type classes.  For the audience, EMH is based on the rational expectations hypothesis; EMH states that markets are rational because any pockets of irrationality are averaged away [i.e., the errors made by the group become smaller than those made by individuals].  In contrast, you also highlight the reality of irrationality that can be observed in markets, something that both Alan Greenspan and Robert Shiller called “irrational exuberance.”  Later, the GFC, or the Global Financial Crisis, painfully hit home that what seems rational for an individual can be dangerously irrational if done collectively.  So my first question is, can we square this circle?  For example, is irrationality just about semantics, or is it something real that not only exists, but because of the collective dynamic, can actually threaten the economic system and may thus not necessarily be averaged away?

HM: To me, Patrick, the answer lies in my view of the efficient market hypothesis.  Again, the efficient market hypothesis says that due to the concerted actions of so many investors, who are intelligent and numerate and computerized and informed and highly motivated and rational and objective and willing to substitute A for B, prices for securities are right, such that they presage a fair risk-adjusted return.  I believe that’s the definition.

But you get into a problem, because when I listed off the qualities that are necessary for a market to be efficient, I snuck in there the economist’s notion of the perfect market and its requirement that the participants be rational and objective. And in investing, they’re not.  That’s really the point.

“Economic man” is supposed to make all these decisions in a way that optimizes wealth.  But she often doesn’t, because she’s not always objective and rational.  She has moods.  And those moods interfere with this arriving at the right price.  So my definition of the efficient market hypothesis is that because of the concerted efforts of all the participants, the price at a given point in time is as close to right as those people can get.  And because it’s as close to right as most of them can get, it’s very hard to outperform the market by finding errors – what theory calls “inefficiencies” and I just think of as “mistakes.” 

Sometimes prices are too high.  Sometimes prices are too low.  But because the price reflects the collective wisdom of all investors on that subject, very few of the individuals can identify those mistakes and profit from them.  And that’s why active investing doesn’t consistently work, in my opinion.  I think my version of the efficient market hypothesis makes it roughly just as hard for active managers to beat the market as does the strong form of the hypothesis, that everything’s always priced right.  But I think mine is more reflective of reality.  I wrote in one of my memos – maybe it was What’s It All About, Alpha? – about a stock that was $400 in 2000 and $2 in 2001.  Now it’s possible – but to me it’s unlikely – that both of those observations were “right.”  Rather, I think they merely reflected the consensus of opinion at the time.

This business – I shouldn’t say “this business”; that sounds derogatory – the idea that inefficiencies will be arbitraged away by the operations of the market ignores one of the key elements that I think describes reality, and that is mass hysteria.  And I think the markets –economies too, but more importantly the markets – are subject to mass hysteria.

I think it was in On the Couch that I said, “in the real world, things fluctuate between pretty good and not so hot.  But in the markets, they go from flawless to hopeless.”  Just think about that one sentence.  If it’s true – and I believe it’s true – that shows you the error, because nothing is flawless and nothing is hopeless.  But markets, I believe, treat things as flawless and hopeless, and there’s the error.

The book I mentioned, Mastering the Market Cycle (I’m going to keep repeating the title in the hope that everybody will buy a copy) . . .  You know, I’m a devotee of cycles.  I’m a student of cycles.  I’ve lived through a half a dozen important cycles in my career.  I’ve thought about them.  I think they dominate what I do.  And I got about two-thirds of the way through writing that book and something dawned on me, a question: Why do we have cycles?

The S&P 500 – I mentioned Jim Lorie – the Center for Research in Security Prices told us almost 60 years ago, that from 1928 to ’62, the S&P 500 had returned an average of 9.2% a year.  Things have been better since then and I think if you go back and look at the whole last 90 years, it’s 10½% a year, the return on the S&P 500.

Here’s a question:  Why doesn’t it just return 10½% every year?  Why sometimes up 20% and sometimes down 20%, and so forth?  In fact – and I included this factoid in one of my memos – it’s almost never up between 8% and 12%.  So if the average return is 10½%, why isn’t the return clustered around 10½%?  Why is it clustered outside the central range?  I think the answer is mass hysteria.

And by the way, the same is true of the economy and mainstream economics, which of course you described as mechanical, and I think that many people would describe as mechanical.  But, certainly, economics is driven by decisions made by people, who are not always rational and objective.  Maybe in theory they’re closer than investors to being rational and objective, but still they’re not always.

But anyway, my explanation for the occurrence of cycles is “excesses and corrections.”  You have a secular trend or a “normal” statistic.  Let’s say it’s the secular trend of the S&P 500.  Sometimes, people get too excited.  They buy the stocks too enthusiastically.  The prices rise.  They rise at more than a 10½% annual rate until they get to a price that is unsustainable.  And then everybody says, “No, I think they’re too high.”  So then they correct back toward the trendline.  But, of course, given the nature of psychology, they correct through the trendline to an excess on the downside.  And then people say, “No, that’s too low,” so then they bring it back toward the trendline and through it to an excess on the high side.

So excesses and corrections: that’s what cycles are about, in my opinion.  Where do the excesses come from?  Psychology.  People get too optimistic, then they get too pessimistic.  They get too greedy, then they get too fearful.  They become too credulous, then they become too skeptical, and so forth.  Oh, and the big one: they become too risk-tolerant, and then they become too risk-averse.

PS: If I can just follow up on that – particularly for our cognitively inclined audience – implied in this you suggest that there might be mental causality, and my next questions are basically also to motivate future research as part of economics revision.  But during your September podcast, in which you revisit the On the Couch memo, you talk about causality and how complex it can be.  And we agree and highlight this in our work.

For example, when Alan Greenspan, in that famous ’96 “irrational exuberance” speech, mentions the complexity of the interactions of asset markets and the economy, and I’m quoting him now: “It chiefly concerns, at least in our view, this dualism of the psychological of the former and the physical of the latter.”  Now, saying this, mental causality is highly controversial and complex in cognitive science, but cognitive science is the area that really studies this.  So, you also specifically refer to Soros’s reflexivity in that context, and as you already indicated just now, but also in your memo, you equate prices almost to psychology.  And finally, we’ve all experienced this dangerous – to the point of existential – tail-wagging-the-dog dynamic surrounding Lehman’s collapse.  So my first question is, if we agree that we will not gain much by identifying yet another behavioral bias, nor by running yet another regression, what would you like to see investigated by cognitive scientists that could potentially lead to more important insights, especially regarding our understanding of the interaction between these two domains of the real and financial economies?

HM: Well, the people at this symposium know much more than I do about how to get to the bottom of these things.  But clearly there’s so much grist for this mill.  Now, exactly how you quantify mood, and so-called animal spirits and irrational exuberance, is beyond me.  I always say, Patrick, and I think I said it in Mastering the Market Cycle, that if I could know just one thing about every security I was thinking about buying, it would be how much optimism is in the price.

When you watch TV and you hear the newsreaders talking about what happened in the stock market today, you get the impression that prices are the result of fundamentals and changes in prices are the result of changes in fundamentals.  And that is vastly inadequate.  (By the way, they always say, “The market went up today because of X” or “The market went down today because of Y.”  I always say, “Where do they go to find that out, because I haven’t found it yet?”  I haven’t found where you go to get an explanation of the market’s behavior, even after the fact.)  But it’s not true that it’s all about fundamentals.  The price of an asset is based on fundamentals and how people view those fundamentals.  And a change in an asset price is based on the change in fundamentals and the change in how people view those fundamentals.  So, facts and attitudes.  Any research that could capture changes in attitudes, I think is important.

Now, what about quantifying these animal spirits?  In one of the more jocular portions of my first book, The Most Important Thing, I include something I called “the poor man’s guide to market assessment.”  I have a list of things in one column, and I have a list of things in the other column, and whichever list is more descriptive of current conditions tells you whether it’s optimism or pessimism that’s governing the market.  There are things like, do deals get sold out or do they languish?  Are hedge fund managers being welcomed on TV or not?  Who does the crowd form around at cocktail parties?  What is the media saying: “We’re going to the moon” or “We’re cratering forever”?  I don’t know how to quantify these things.  But these are among the very important things that I listen to in order to figure out where we stand in the cycle.  And I believe where we are in the cycle plays a very strong role in figuring out where we’ll go next.  (In fact, take the title of my second book, Mastering the Market Cycle.  When I was thinking about writing it, it was called Listening to the Cycle. “Listening” in the sense of taking our signals from where we are in the cycle.  “Listening” also in the sense of obeying.  The publisher thought we’d sell more books if the title implied the book would help you master the market cycle.)  But I, as a practical investor, try to figure out what’s going on around me.

Now let’s go back.  I didn’t do what I should have, because I didn’t answer Russell Napier’s real question: can I name two episodes that showed this kind of thing in action?  I was glad to have the questions in advance, because it allowed me to think about the two episodes I want to propose.

In the spring of 2007, I wrote a memo called The Race to the Bottom.  This was when the subprime mortgage mania was at its apex, I think, and when the logs had been stacked in the fireplace for the conflagration that became the Global Financial Crisis.  It happens that I was driving around England in the fall of ’06 – maybe November or December ’06 –and I was reading the FT (I mean I wasn’t driving and reading; I was being driven so I could read), and there was an article in the FT that said that, historically, the English banks had been willing to lend people three-and-a-half times their salary in a mortgage.  But now, XYZ Bank announced that it was willing to lend four times your salary, and then ABC Bank said, “No, we’ll lend five.”  And that bidding contest – to make loans by lowering credit standards – seemed to me to be a race to the bottom.  And I wrote that markets are an auction place where the opportunity to make a loan, or the opportunity to buy a stock or a bond, goes to the person who’s willing to pay the most for it.  That is to say, get the least for his money, just like in an auction of a painting.  And so, in this case, the bank that was willing to have the lowest credit standards and the weakest loans was likely to win the auction and make the loans: race to the bottom.  And I said this is what happens when there’s too much money in the hands of providers of capital and they’re too eager to put it to work.  Mood!  And, of course, we all know the Global Financial Crisis ensued.

Now fast forward from February ’07 to October ’08: Lehman Brothers goes bankrupt on September 15, 2008, and now, rather than being carefree, the pendulum has swung, and people are terrified.  Rather than seeing risk as their friend, as in, “The more risk you take, the more money you make, because riskier assets have higher returns,” now people say “Risk bearing is just another way to lose money.  Get me out at any price.”

So the pendulum swung, and of course people’s optimism collapsed, the S&P 500 collapsed, and the prices of debt collapsed.  So I wrote a memo right around October the 10th of ’08 – maybe that day was the all-time low for credit, I don’t know exactly – that was called The Limits to Negativism, based on an experience I had. I needed to raise some money to delever a levered fund that we had that was in danger of melting down due to margin calls, and I went out to my clients.  I got more money.  We reduced the fund’s debt from four times its equity to two times.  Now we’re again approaching the point where we can get a margin call.  Now I need to delever it from two times to one time.  I met with a client who said, “No, I don’t want to do it anymore.”  And I said, “You gotta do it.  These are senior loans, and the default rate on senior loans has been infinitesimal over time.  There’s potential for a levered return of 26% a year from what I consider incredibly safe instruments.”

This client – excuse me if I belabor this, but I think it’s interesting – this client said to me, “What if there are defaults?”  And I said, “Well, our historical default rate on high yield bonds – which are junior to these instruments – is 1% a year.  So if you start with 26% and you take off 1% for defaults, you still get 25%.”  So she said, “What if it’s worse than that?”  I said, “The high yield bond universe default rate has been 4% a year, so you’re still getting 22% net.”  She says, “What if it’s worse than that?”  And I said, “The worst five years in our default experience is 7½%, and if that happens, you’re still getting 19%.”  She says, “What if it’s worse than that?”, and I said, “The worst year in history is 13%.  If that recurs every year for the next eight years, you’ll still make 13% a year.”  She says, “What if it’s worse than that?”  And I said, “Do you have any equities?”  She said, “Yes, we have a lot of equities.”  I said, “If we get a default rate on high yield bonds of more than 13% a year every year into the future, what happens to your equities in that environment?”

I describe myself as having run back to my office after that meeting to write that memo, The Limits to Negativism.  What I wrote there was that it’s very important when you’re an investor to be a skeptic and not believe everything you hear.  And most people think being a skeptic consists of dealing with excessive optimism by saying, “That’s too good to be true.”  But when it’s pessimism that’s excessive, being a skeptic means saying, “That’s too bad to be true.”  That particular investor couldn’t imagine any scenario that couldn’t be exceeded on the downside.  So, in other words, for that person, there was no limit to negativism.

And when I conclude that the other people in the market, the people setting the market prices, are excessively negative and excessively risk averse, then I – an inherently conservative person – and my partner, Bruce Karsh, who runs our distressed debt funds – also an inherently conservative person – we go crazy spending money when we conclude there’s excessive pessimism, fear, and risk aversion incorporated in asset prices [meaning they’re lower than they should be]. So it’s not just the mechanical aspects that determine market prices – it’s psychology.  It’s mass hysteria, which comes in waves from time to time, that leads to market cycles that prove excessive.

3. This Diamond Company Wants To Help Carbon Capture Take Off – Maddie Stone

That company is Aether, a lab-grown diamond startup that just raised $18 million in a funding round led by Helena, a “global problem solving organization” that includes both a for-profit investment and nonprofit action arm. Lab-grown diamonds are a hot market, and there’s no shortage of companies claiming that these synthetic gems are more ethical or environmentally friendly than their Earth-mined counterparts — and there are even other companies also focused on making diamonds using carbon dioxide from the air. But Aether’s claims are backed up by some ambitious facts about its operation: not only is it making diamonds in a process powered by clean energy — it’s pulling an additional 20 metric tons of CO2 out of the atmosphere per carat it produces.

While the cost of capturing all that carbon would be high for a company selling, say, cement, it’s one the luxury jewelry brand says it can easily absorb. And the world needs businesses that can pay for so-called direct air capture and still generate a profit if the nascent technology is ever going to make a dent in climate change…

…Aether, which also works with Climeworks, wouldn’t disclose how much it’s paying for direct air capture services. But it says it can transform one ton of captured CO2 into “millions of dollars’ worth of diamonds”. On a per carat basis, those diamonds, an ultra high-purity breed known as Type IIa diamonds that are difficult to find in nature, sell for anywhere from $4,900 to over $10,000. Shearman says this price range is higher than many competitors in the lab grown space and closer to that of mined diamonds because of the additional work that goes into making the fabrication process as clean as possible.

That process starts with Aether purchasing carbon dioxide from Climeworks’ facility in Switzerland and shipping it to the United States, where the diamonds are grown. Aether puts that CO2 through a proprietary process to convert it into high purity methane, or CH4. That methane is then injected directly into the company’s diamond reactors, where a method known as “chemical vapor deposition” is used to grow rough diamond material over the course of several weeks.

The chemical vapor deposition process involves heating gasses to very high temperatures under near-vacuum conditions, and considerable energy is required to do so. Shearman tells The Verge that this process and other manufacturing stages are powered entirely by carbon-free sources like solar and nuclear. Once the diamonds finish growing, they’re shipped to Surat, India, where they’re cut and polished before being sent back to New York City’s diamond district for sale…

…Aether only needs a relatively small amount of carbon dioxide to make the diamonds themselves — think fractions of grams rather than tons. Then, for every carat of diamond it sells, the company says it removes an additional 20 metric tons of carbon from the air, using a mix of direct air capture and other carbon removal methods that involve long-term carbon sequestration. Shearman says the company based this commitment on the fact that the average American has an annual carbon footprint of approximately 16 metric tons, meaning most customers can expect to roughly cancel a year’s worth of personal emissions by purchasing an Aether diamond. “It’s something that has proved to be difficult but doable, and we’re really proud to be able to do that,” he says.

Aether started shipping its first diamonds to customers in the middle of 2021. While Shearman wouldn’t offer specific sales figures, he says that the company produced “hundreds of carats” of diamonds last year, and this year plans to produce thousands. Shearman described the $18 million in Series A funds raised by Helena as “the fuel that’s going to enable us to increase our production footprint this year.”

4. An introduction to Integrated Photonics – Jessica Miley

Integrated Photonics (IP) is the use of light for applications traditionally tackled by electronics. It can be used in a wide range of areas including telecommunications such as 5G networks, biosensors for speeding up medical diagnosis, and in automotive where it is used in LiDAR. IP consists of integrating multiple photonic functions on a Photonic Integrated Circuit (PIC) fabricated using automated wafer-scale generic integration technology over silicon, silica, or Indium Phosphide (InP) substrates. Integrated photonics dramatically improves the performance and reliability of these photonic functions while simultaneously reducing the size, weight, and power consumption.

A good introduction to IP is by understanding its similarities and differences with traditional electronic circuits. Where electronics deal with the control of electrons on a chip, photonics does the same with photons. Photons are the fundamental particles of light.

Conventional integrated circuits (ICs) conduct electricity by allowing the flow of electrons through the circuit. Electrons are negatively charged subatomic particles that interact with both other electrons and other particles. These interactions slow electrons down as they move through circuits, this limits the amount of information that can be transmitted; it also generates heat, which in turn causes energy and information losses.

Photonic integrated circuits (PICs) use photons. Photons move at the speed of light with almost no interference from other photons. This greatly increases the bandwidth (the data transfer rate) and speed of the circuit, without big energy losses making PICs significantly more efficient than their IC counterparts.

Integrated photonic components use “waveguides”, which confine and direct the light in the desired directions (by total internal reflection), much the same way as metallic wires do for electrical signals. A PIC provides functions for information signals on optical wavelengths typically in the visible spectrum or near-infrared 850 nm-1650 nm.

The elements on a PIC are connected via waveguides. The chip elements can be both passive (e.g. couplers, switches, modulators, multiplexers) and active (e,g amplifiers, detectors, and lasers). These components are integrated and fabricated onto a single substrate, which creates the compact and robust photonic device.

A key difference between electronic circuits and PICs is in the primary device that is used for fabrication. In an electronic integrated circuit, the main device is the transistor. But, in PIC, there is no particular main device that dominates in the fabrication. According to its application, the PIC will be designed with a range of fabrication devices. This integration presents opportunities to reduce current bulky, complex, and expensive optical systems in an integrated chip-scale way that has increased stability and robust operation, reduced size and power consumption, and cost-effective large-scale fabrication of even complex circuits.

5. It’s worse than you think – Oliver Burkeman

Here’s a surprisingly useful question to ask yourself next time you’re stumped by a problem, daunted by a challenge, or stuck in a creative rut: “What if this situation is even worse than I thought?”

This question, I admit, appeals to my taste for bloodyminded contrarianism. But its real value is that it expresses what I think of, more and more, as a fundamental truth about human psychology: that we often make ourselves miserable – and hold ourselves back from what we might be capable of achieving – not because we’re too pessimistic, but because, in a sense, we’re not pessimistic enough.

We think of certain kinds of challenges as really hard when they are, in fact, completely impossible. And then we drive ourselves crazy trying to deal with them – thereby distracting and disempowering ourselves from tackling the real really hard things that make life worth living.

A case in point: you feel overwhelmed by an extremely long to-do list. But it’s worse than you think! You think the problem is that you have a huge number of tasks to complete, and insufficient time, and that your only hope is to summon unprecedented reserves of self-discipline, manage your time incredibly well, and somehow power through. Whereas in fact the incoming supply of possible tasks is effectively infinite (and, indeed, your efforts to get through them actually generate more things to do). Getting on top of it all seems like it would be really hard. But it isn’t. It’s impossible…

…Anyway, you get the picture. And you probably get the point, too – which is that when you grasp the sense in which your situation is completely hopeless, instead of just very challenging, you can unclench. You get to exhale. You no longer have to go through life adopting the brace position, because you see that the plane has already crashed. You’re already stranded on the desert island, making what you can of life with your fellow survivors, and with nothing but airplane food to subsist on. And you come to appreciate how much of your distress arose not from the situation itself, but from your efforts to hold yourself back from it, to keep alive the hope that it might not be as it really was.

And then, crucially – because some people tend to mistake this for an argument for nihilism, or a life of mediocrity, when it’s really the opposite – that’s precisely when you can throw yourself at life’s real hard challenges: the impressive accomplishments, bold life choices, and deeply fulfilling relationships. You get to live more intensely, because you’re no longer making your full participation in life dependent on reaching some standard – of productivity, of certainty about the future, of competence, etcetera – that you were never going to reach in the first place.

6. Alex Danco – Tokengated Commerce – Patrick O’Shaughnessy and Alex Danco

[00:05:11] Patrick: Can you give an example that is not at all Shopify related on interoperability and the power of platforms from history that people might be familiar with?

[00:05:20] Alex: Let’s talk about interoperability for a second. People use this to mean a lot of things, but in general, what it means is that imagine that you have two levels of a system where one level of the system needs to interact with the other level, and you have n players on level one, and you have n players on level two, they both need to be able to work with each other in a way that just works fluently without really having to talk to each other very much. I’ll give you an example, which is the shipping container. I know you love talking about shipping containers on the show. I have a factory that makes inputs and you have a factory that takes those inputs and you build something value added out of them. And I need to ship it from me to you, how do we do this? Well, we could work together on figuring out, what is the shape of box that best fits this part? And how do I work with a shipper to make sure that box is going to go on their boat or on their plane effectively?

And how do we negotiate all these things? Or we could just put it all in the same, exactly standardized 40 foot box that goes on boats that know how to fit exactly that box on it, and through a supply chain that knows how to deal with this thing and then out the other side with neither of us ever having to even know about each other or what we’re putting in. This is this idea of a constraint that de constrains. It’s a very, very common motif that you see in interoperability, which is this idea, a free for all is actually no freedom at all. A very, very common lesson here. I can give you all sorts of examples throughout history of saying, if you give people no rules whatsoever, and then everybody tries to work with itself, that’s a mess, nothing ever gets done. However, if you have these really nice constraints or conventions or platforms or standards, many different angles of approaching this problem of interoperability, you can actually unlock something pretty magical, which is this community of n people on one side and this community of n people on the other side can actually create n times n different things without needing n times n different bits of glue stitching all of those things together…

…[00:12:05] Patrick: Can you think of an example where that’s violated, where someone’s trying to create a constraint standard but there is too many degrees of freedom and it failed?

[00:12:13] Alex: Sure, that’s almost every standard. Most standards do not succeed. And the reason why they do not succeed is because they just don’t grasp the problem entirely correctly. There’s that XKCD joke, which is like, “There are 12 standards, what we need is a common standard for how everybody represents this. The next day, there are 13 standards.” Standards work is very, very difficult to achieve because so many things have to go right. But if you look across, even in the history of computing, there are several incredible reference standards that are held up. Unix is one of them. The IP internet protocol is probably the greatest one of all of them, it is a very, very, very restricted way in which you can represent the information going through the internet, but what it means is that any webpage, any application, any whatever can submit something that can then get communicated over any kind of communications network. It could be sent over copper wire. It could be sent over ethernet. There are some aficionados that have sent message by carrier pigeon over internet protocol. You can do it. It doesn’t matter. As long as it runs through internet protocol, anything will work on either side. This overall design, I know you talked with Tobi the last time he came on the show, this overall design is something called hour glass architecture or narrow waist architecture. It’s one of the most powerful ideas in building things. This idea of, if you want many, many things to be able to inter-operate with many, many other things, there needs to be a narrow waist that is as constrained as possible between them.

A very, very important idea, and so Shopify really, really understands this, as evidence through how we built Liquid and how every app developer can make apps that works with every theme developer and they don’t talk to each other and you don’t need a piece of custom glue like you would with enterprise software, it just works. The same with anything can go into the internet protocol and it can be communicated over anywhere. Another good example of a narrow waist in computing is the X86 architecture, which Intel made. Anybody can submit instructions to this instruction set, and then it can be executed on any processor that knows how to deal with the X86’s instruction set, but there’s this common waist that it goes through. And I’m including Intel in there just to show that there are a couple of different ways that a narrow waist can come about. It could come about through a bunch of different academics getting together, it could come through with a standard body, but also it could come through when one monopoly says so. In the case with Intel, there’s not any one way to do this, but they’re hard to achieve, and when you do, you have something that’s going to last for a very long time.

[00:14:24] Patrick: It’s sort of obvious with the examples you’ve given, whether it’s the shipping container, or the internet itself, X86, ISO, whatever, that when you get one of these right, it crazy amount can be built on top of it in ways that you could never envision when you set the standard, the creativity that can exist on top of it is fast and unpredictable. That brings us to the topic at hand, which is tokengated commerce, maybe we need to start with why blockchains are potentially interesting narrow waists. But before we do that, tokengated is two parts, token and gated, give us a high level description of why you were doing this, why you were spending your time on it, why Shopify is heavily invested in this notion? This is a new idea, and I want to understand it at a high level.

[00:15:03] Alex: First, let me actually tell you what is tokengated commerce, because at its heart, it’s actually a very simple idea. Tokengated commerce means, here’s a product and I’m going to put a gate in front of it. And if you want to pass the gate, you need to show me a token that says I pass the gate. More generally speaking, what does this look like in practice? Well, what it looks like is, “Hey, I’m a brand. I have all these cool products. I want to make them very exclusive. If you want to unlock the product, you have to connect your wallet, a crypto wallet, sign a transaction showing that you own this wallet and this wallet owns,” let’s say, “the right NFT.” Because I own this NFT, I can unlock this product. Or it could be, because I own this NFT I unlock early access to a drop. I can get to the drop 15 minutes earlier than everybody else. Or because I own the rare version of this NFT, I’m able to get the rare version of the hoodie. Anybody can get the black version, but if I have the rare NFT, I can get the red version and that red version is cool. Or because I own this NFT, I’m able to buy this product and you can only buy one product per number of NFTs you own. These are all various ways of implementing this simple idea, which is, there is context somewhere. And that context is going to influence how my business wants to treat you. What if, as the buyer, I can bring that context with me and sign with it, proving I am me and here’s how I show I have some ownership over this bit of context?

And my storefront can respond to this and say, “Okay, now that I see that you’ve signed for this bit of context, my storefront is going to respond to that context by doing something appropriate.” Maybe it’s unlocking a product. Maybe it’s giving you really access to a drop. Maybe it’s letting you get into a party. It could be anything. It could be, “Here’s something live and in person. Here’s access to 15 minutes of a live stream with me.” It could be anything. It doesn’t just have to be products. This idea of token gating, defined it very, very simply is it’s a kind of behavior that is very, very natural. It’s how do I get into the exclusive thing? How do I show that I have done the challenge of gaining access? How do I get the thing that I want to get that is hard and feels like a reward? These are very, very old ideas in commerce, this idea of commerce is a challenge that the buyer and the merchant do together. And token gating, we are finding, is an incredible foundational piece of UX for the basic idea of the most meaningful kind of commerce is a challenge that you do together.

[00:17:08] Patrick: If you think about the many, many aspects of this, I want to start with the token itself, because if you think of non-fungible token, which have been popular, you own a Bored Ape, you own a Crypto Punk, you own whatever, piece of art, whatever, you could see a world where brands build specific product lines that tailored to you have to own one of these things that are already independently exclusive. So we’re sort of riding the scarcity of Bored Apes, let’s say, as a cool way to create something custom for them. Talk to me what you’ve learned here. Do you think that most merchants will outsource the scarcity function of the tokens themselves, or are you going to empower them to also create their own tokens that trade? It just seems like the world has coalesced around a small number of the most popular projects. Like all the examples you hear are, “If you’re an owner of one of those special things, we’re going to treat you differently, because it’s like you have a black card or something.” So start with the token piece. How do you think it will work?

[00:18:00] Alex: In that question, there were like three or four really good questions. So I want to try to answer them in the right order here. First, if you look at these NFTs, what are these things? What are they any good at representing? What do they all have in common? Let’s break down a couple of common aspects of these NFT projects. One aspect of them is these entities are owned by people, and the way that they own them is through their wallets. What is a wallet? Well, at a very basic level, a wallet is I have my public address and I have my private key and I sign my private key to show that I am who I am. My wallet address is associated with this token on this smart contract, which means I own this ape. First of all, let me just present a very basic observation, which is what are people doing with their wallets? Well, they’re connecting them everywhere. They’re connecting them to discords, to get into the discord. They’re connecting them to adapt. They’re connecting them to any kind of application that is asking them to authenticate in a certain kind of way. Now what we’re seeing is people want to connect these wallets to storefronts to say like, “Hey, I’m not a fungible buyer. I’m a non-fungible buyer because I have this token.” We really like saying NFTs aren’t a kind of product. They’re a kind buyer, a non-fungible buyer. I really want to get this into people’s minds. NFTs fundamentally to us are an input for commerce. They’re a piece of context that the buyer brings with them when they show up to the storefront. It can also be an outcome of commerce. We can do a commercial transaction where one of the outputs of this commerce as I’ve been to new NFT and give it to you. It doesn’t have to be an NFT either. It could be an ERC 20. It could be any number of other things.

These are very, very flexible ideas, but even this very basic thing of, “I have an NFT. I connect it to the storefront, it unlocks a product. Then I go check out. And maybe on the other end of the checkout, you want to sell me another NFT and I may buy that also. Then maybe I’ll use that somewhere else.” All of these are very, very interesting kinds of outputs and inputs to what we call commerce. But as you said before, I want to make sure that I’m answering your original question here, which is over the last year or two, there was this explosion of communities who were all issuing these tokens and everybody was getting in. “Oh, this is cool art.” These are going to have utility. Who are all these communities? And now what we’re seeing is yeah, a lot of these communities didn’t really have much of a game plan, but some of them do. And the ones that do are actually turning out to be formidably impressive media companies, because they have this fascinating way of creating fan bases. One way to look at NFT is this is a new way of creating a fan base, but it’s creating a fan base on the very beginning. You have to have some idea of what you’re doing with your brand. But nonetheless, the specific example of a merchant that we work with closely is Doodles. Doodles is one of the premier NFT brands. They understand fully that they are merchants and they are brands and they are media powerhouses. They understand that that’s the kind of business that they’re building. And they see these tokens as a new fundamental piece of what is it that their fans have that they can bring with them and connect into places in order to bring all that context with them…

…[00:28:14] Patrick: I’ve gone way too far into the conversation without asking what the literal mechanic that Shopify is building will let people do and won’t let them do. Is it as simple as saying, “If I’m a merchant, you can sign with whatever and I’m going to go through a menu and pick the tokens that I want to let and tie them to a certain thing and then you handle the rest”? What is literally going to be the thing that you offer?

[00:28:32] Alex: That’s actually a very good way to put it, which is that the number of things people want to do with token gating is very diverse and very hard to predict. We cannot know what all of them are. But you know what? We don’t have to. We’re a platform that is what app developers do. This is how Shopify is built. This is exactly like the problem of saying, “Well, there are many themes in the theme store and there are many apps that want to make mechanics. Do I have to think of every single thing that an app could do so that themes can know about it?” No. We just build our platform in a way where we present the right constraints and the right formats for saying, “Hey merchant, you want to do some token gating. Well, there are a lot of different ways that you might think your token gating wants to do. Some people want to token gate for discounts. Some people want to token gate around mechanics to do a cool sneaker drop. Some people want to token gate so that people can buy variants on a product to correspond to variants of their NFTs.” All of these are perfectly valid ways to do token gating and we’re not going to come up with what they are. What we are doing is we are creating a common platform for app developers to go make whatever kind of token gating rules you want to make in a way where those token gating rules can be presented in any selling surface where the merchant wants to go.

Maybe they want to sell on the online store, and that’s great. Maybe they want to sell at a retail point of sale environment. We have merchants doing this now. They’re doing a popup store and they’re an NFT brand. They’re like, “Oh, I only want to sell this thing in my popup store to people who have this NFT and can sign for it. And I want to do it on retail POS.” No problem. Some people want to buy things on mobile, and we have the shop app, which is our mobile app for shopping, and there’s some merchants who want to set up a little token gated store in the shop app that works really well on mobile. We have a product called GM shop that I’ll tell you about it in a minute that is exactly that. But your general question of what is the product that Shopify lets merchants do? It’s, well, you can do anything because we’re a platform. That’s the hard work of being a platform is coming up with what are exactly the right constraints that anybody can make inputs to it and anybody on the other side can read them and go carry out token gating instructions if we’ve come up with exactly the right constraints in the middle. The slogan I like to say when people say, “What are you doing with your life?” I say, “I’m making Shopify wallet aware.” That’s what I’m doing. Wallet awareness is not a single thing. It is an idea around everybody accepting a certain set of constraints that become deconstraining. They’re constraints that become liberating…

…[00:38:58] Patrick: You started to answer a key part, which is if all you wanted was an unlimited amount of people to have a certain access then pure text is great. If you want to limit it somehow obviously then the non fungible nature of the tokens becomes very important. So I get it. And you could certainly see the world normalizing too in your browser, you have a wallet, and you’re constantly like getting shit in your wallet from different people and they represent different things and blah, blah, blah. So now let’s talk about demand, this big topic of what is demand? Where does it come from? How does it tie into this whole story? And why is this new primitive for unlocking demand?

[00:39:32] Alex: Demand is one of my favorite topics because it’s simultaneously such a basic thing that everybody has opinions about. But also it’s one of the hardest things to conjure. You’re not a business until you have demand. A business plan is not demand. Nothing is a substitute for demand. Demand is the thing. Every business owner knows this. What is this mysterious thing, and how do I get it, and once I have it, how do I turn it into more? Before I was at Shopify, before I worked in DC, before we knew each other, long before that, I was in a band. I was in a band called The Fundamentals. We were on a label called Stomp Records out of Montreal, it’s a ska punk label. We never made it big, but we toured around. We had a record deal. We were trying to make it big. This is what we were doing with our lives.

And when you’re in a band, your business model is you lose money recording music, so that you can break even selling concert tickets, so that you can make money selling merch. That is how it works. You are a merchant. What you sell is apparel, basically, to your fans and you give them a reason to buy your stuff. Everything else is more or less a loss leader for your merch business when you’re at that size of band, anyway. I’m sure Taylor Swift makes money at all slices of the pie, but even like the Taylor Swift merch empire is, this is massive, massive, massive business, because there is demand for Taylor Swift merch. How do you make that demand and where does it come from is the question of being a retailer or the question of being a merchant. I can tell you honestly, when you’re a band, demand is something where it exists in two states. If I’m a band and I have these fans that are all out there in the world, they like me in a very sort of abstract way. They listen to my music. They’re thinking about me sometimes. The demand for them to buy my stuff is not really activated. It doesn’t exist in a more tangible form. The proof of this by the way is if you look at musicians merch businesses, let me ask you what percentage of a band’s merch do you think is sold online as opposed to at shows?

[00:41:08] Patrick: 50%.

[00:41:10] Alex: Almost none. So the rule of thumb is that no matter how big you are, your online merch business per year is about the same as two weeks of tour dates.

[00:41:19] Patrick: Oh, wow.

[00:41:19] Alex: Yeah. It is a very, very, very strong ratio. This is more or less universal whether you’re a small band or a big band or whoever you are. This is not to say that people don’t like Taylor Swift, unless they’re in the Taylor Swift concerts. No, they like Taylor the whole time. But you need there to be a precipitating event to cause people to be compelled to buy the merch now. The demand has taken a more meaningful form. It’s almost as if the demand isn’t like a gaseous state and then it becomes more active when certain things happen. And I want to tell you about those things because there are some universal rules to them in how culture works. When you’re a band, you have all these fans and they exist and they know who you are. But then when you come to town, what you do is you play a show. You sell tickets to the show, people buy the ticket, and they enter in this space, and this is very intimate space. And you do a challenge together called dance to the music. On completion of the challenge everybody lines up to go by the merch. This is a universal rule of music. There is a very, very specific orchestrated sequence of events that causes people to buy your stuff. Everybody who has active experience with being a certain kind of culturally cool merchant will recognize their version of this. Demand isn’t enough. It has to be activated demand. It has to be awakened by something. And the thing that awakens demand is a challenge of some sort. I think you were posting about this on Twitter or something. Challenges are the things that make life meaningful. They’re the thing that give us identity. They’re the thing that give us purpose. They’re the thing that makes us feel good about ourselves. Challenge and overcoming the challenge. Demand in absence of challenge is cheap and stupid.

It’s not necessarily stupid, but it’s baseload demand. I have baseload demand for paper towels. That’s fine. I can get them from the corner store. I can get them from Amazon. That’s fine. But the more meaningful kind of demand that actually is something meaningful to my life, that kind of demand is only awakened by a challenge. It might be the challenge of being in a particular store and really, really talking to a merchant and figuring out what I want. It could be the challenge of going to a show. It could be the challenge of being in a cool collab or whatever it is. But ultimately demand has to be activated by something and that thing is challenge. What kind of challenges are the things that people really care about? Well, the basic challenge that we care about is identity and group association. I’m a part of this group. I have these peers. I’m living up to a certain challenge that the peer group does. This is through the basis of all culture. That kind of culture is the basis of a certain kind of retailing called products that people buy to be cool or products that people buy to be a part of a group or products that people buy because they have some sort of meaning to them. The number of different kinds of products like this are quite varied. It’s not just t-shirts that bands sell. It could be memberships to something. It could be getting tattoos. Everybody has this thing that they’re really, really into. But ultimately demand, I want to bring this back to this sort of nebulous concept of demand, is something that people have understood as a part of commerce for thousands of years, but only up until recently that demand was always in person. There’s a challenge that the buyer and the merchants come together to flesh out what context is the buyer bringing with them? Under what circumstances does this demand unlock and activate the challenge? This is something that people naturally do face to face really well.

But online, it’s really hard to do this. It’s hard to show up to an online storefront and bring a vibe with you, do a challenge together, or engage in any of these things. I would say the first mechanic that people online came up with that actually activated this was the drop, the concept of, “Okay, at noon the sneakers are going to drop and you have to get them as fast as possible.” That’s fun. That is a great example of how you sell things. That’s how you get demand to actually convert into purchases is you do a drop or you make an exclusive thing or like you create a challenge and you motivate people to get behind the challenge. I believe it was Modest Proposal was on your podcast a long time ago, talking about eCommerce and this idea of getting all the friction out of commerce. That’s really not it. There’s some kinds of friction that are bad, but there are actually some kinds of friction that are really good. I talked to you about this in the Shopify podcast. This idea of a challenge is required to turn demand into buying. Different cultures do it in different ways, different kinds of retailers do it in different ways. A luxury brand like Gucci will do this in a very different way than a fast fashion brand like Forever 21 will do it. They’re obviously very, very different retailers. They move different kinds of merch for different kinds of price points. But they’re doing the same thing. Look at a really, really well run retailer like Aritzia. All of Aritzia is keyed into getting this latent demand to come in the door, activating it around this certain kind of challenge, and then converting it into incredible brand loyalty. That’s what really powers these businesses. Same on the merchant side, you have the challenge of tack. How do you convert that into something that will produce LTB for a very, very long time?

7. TIP457: Why The Dollar Is Not Collapsing w/ Jeffrey Snider – Trey Lockerbie and Jeffrey Snider

Trey Lockerbie (00:02:14):

So, we have a whole global monetary system right now that I think a lot of people would call a Petrodollar system, and we’re going to work a little bit backwards from what that means. There’s also the Eurodollar system in play that people may or may not be as familiar with. So, I want to actually start there with the Eurodollar. It’s a big loaded question, but going back to basics here, just simply tell us what is the Eurodollar?

Jeff Snider (00:02:39):

Well, technically speaking, and going back all the way to the beginning, Eurodollar refers to a very specific term, and it means US dollars on deposit outside the United States. In the early days, it actually took the form of actual cash deposit, physical Federal Reserve notes, bills, cash bills and things like that, that found their way mostly to Europe, but not just exclusively to Europe, thus the term Eurodollar. It doesn’t have anything to do with the European common currency. It is, again, the term Euro simply means offshore, because this goes way back to the 1950s and 1960s long before the European common currency was ever introduced. So, whenever you hear the term Euro and then attached to a currency denomination, what that simply means is money that the banking system uses outside the jurisdiction of the United States or even any of the other currency denominations that are floating around in it.

Jeff Snider (00:03:31):

So, there are things like Euroyen, for example, which means yen outside of Japan, that’s in this offshore currency system or even something like the Euroeuro, which is offshore euros. So, essentially, after beginning sometime in the 1950s and spreading through the 1960s, we have a huge, very much comprehensive global monetary system that undertook the roles of the reserve currency, global reserve currency, but it’s not actual cash. It’s not actual currency. There’s no money in it. It’s a virtual ledger system, a distributed ledger system that the global banking system operates and therefore has undertaken the roles of a reserve currency because banks have been able to flexibly and dynamically respond to the world in which they live in.

Jeff Snider (00:04:18):

So, for the last 60 years, this Eurodollar system has been essentially the global monetary reserve. And because it’s offshore, it’s outside the jurisdictions, not just the US, but pretty much anywhere, which is kind of a strange concept because these banks are located and doing business someplace. They’re physically located somewhere. But they have located and they have been able to take advantage of various regulatory blank spots, regulatory boundaries. So, this currency system has been able to grow and expand basically outside the reach of national governments, national regulators, bank regulators, whatever it may be and operate throughout the rest of the world. Again, so the point being to create this global reserve currency arrangement that goes back a long, long time.

Trey Lockerbie (00:05:05):

That last point there, what I hear you describing would maybe otherwise be called something like shadow banking, right? Or is that correct? And if not, what is a shadow bank and what is the shadow economic system?

Jeff Snider (00:05:16):

Well, shadow banking is part of it. That’s more about some of the non-bank participants who actually in this global monetary arrangement. I like to use the term shadow money, because they’re actually monetary forms that they don’t show up in any of the statistics. They don’t show up in any regulatory discussions. They’re not involved in any of the mainstream policy framework, because, again, this is outside the United States, it’s outside of every regulatory regime on earth and regulators are not too keen about people knowing about this vast, huge monetary system existing outside of their reach when their entire monetary policy and really political existence, it relies upon the idea that they are very much in control of this system and this arrangement.

Jeff Snider (00:05:57):

So, it’s outside of everyone’s reach, but also the ways in which these banks operate monetarily as well as credit has evolved and changed so that you have monetary forms like currency swaps, for example, that function every bit the same as cash would, except a currency swap doesn’t fit into a monetary aggregate, it doesn’t fit into any sort of quantitative measure, nor qualitative understanding. It doesn’t even fit into the bank balance sheets in a intuitive way. In essence, this is a virtual ledger money system, that’s a shadow money system because of the way the banks operate on their balance sheet.

Trey Lockerbie (00:06:32):

We’re going to explore the significance of that in a minute, but let’s keep with the basics for a minute. So, let’s say the US, we were on a gold standard for a very long time. We had to pay for some wars and stuff and we had to kind of break our promise that was the dollar was backed by gold, we kept changing the money multiplier over time. And at some point, it was unfeasible to continue on with the gold standard. So, like 71-ish, Nixon says, “Hey, you know what, we’re going off the gold standard into this fiat system.” And a lot of people said, “Okay, well,” there was this meeting with Saudi Arabia and we developed this agreement with them to now produce something called the Petrodollar system. And that’s what a lot of people believe we’re operating on today. But is that correct, Jeff? What’s your opinion?

Jeff Snider (00:07:12):

The short answer is no. And it’s a common misperception, because you can understand why. The Bretton Woods system, which was a quasi-gold-backed system, a commodity-based monetary system that grew out of World War II, in the ashes of World War II, where Harry Dexter White and John Maynard Keynes in particular said, “We can’t just have an international currency arrangement because nobody will accept it. So we need to tie this international currency to some national reserve.” And historically speaking, people wanted to use gold, because gold for various reasons that we don’t need to get into here.

Jeff Snider (00:07:40):

So, you had the Bretton Woods system 1944, which always had this inherent flaw or inherent tendency in it as Robert Triffin called it in the late 1950s, eventually become called the Triffin’s paradox or Triffin’s dilemma, which was that in order to operate a global reserve currency, you need to have enough currency floating around the world to be effective. Because what is a global reserve currency? It’s a mediating currency where vastly different systems can connect to each other through this third-party mediating system or mediating currency so that trade, financial flows, all of the free market capitalism that we’ve come to love and honor, those things can happen in a very efficient fashion so that we can have a globalized, highly efficient economic system.

Jeff Snider (00:08:24):

The problem was by tying this international currency and using, for example, the US dollar or the British pound and backing that currency with national stores of physical bullion, there was always going to be the problem where there’d be too much currency needed outside the US, which would then lead to anyone ending up with that currency, redeeming the paper for national reserves. Eventually the national reserves of gold would be drained from the system and Triffin’s paradox would be that once those reserves were drained, the whole thing would just fall apart, which by the way, came close to happening in the late 1950s.

Jeff Snider (00:09:00):

So, we’re talking about not even really 15 years into Bretton Woods, it was already falling apart. So, this is where the Eurodollar steps into it, because it divorces the national currency from the national store of reserves. So, long before 1971, you had this global monetary arrangement, because it was reserveless, because it was ledger money that it began to undertake the roles of the former Bretton Woods system as it broke apart. So, by the time you get to August of 1971 and President Nixon closing the gold window, the Eurodollar had long undertaken all of those roles of the reserve currency before that.

Jeff Snider (00:09:36):

So, August of 1971 represented nothing more than the symbolic end of Bretton Woods when the functional end started a decade and a half before that. So, in terms of the Petrodollar, it wasn’t like we moved from a commodity goal-based monetary system to a oil-based system in the 1970s. We moved off of the commodity-based monetary system long before that. And it had superseded the Petrodollar, the stuff that happened in 1973, for example, and basically all of the functions of the Eurodollar were up and running for more than a decade by then. And even the Eurodollar system itself had become absolutely huge and immense by the early 1970s.

Jeff Snider (00:10:15):

So, the transition took place into something that was a ledger of ledger virtual currency system long before then. And it took place into this offshore bank-centered sort of blank canvas where banks could experiment in all different types of money, so that we transitioned long before from a commodity gold exchange system, the Bretton Woods, to this virtual reserveless currency system under the Eurodollar over a long period of time before we even get to 1973…

…Trey Lockerbie (00:13:59):

So, how much of the narrative that we’re currently operating on comes to us from our actual own Federal Reserve, or even say the media or education around the system that we’re currently in? Because as I understand it, your research has led you to study papers from internal employees at the Fed and elsewhere. And some of them know what’s going on. Some of them are discovering what’s going on through their work. And others just have no clue maybe because they’re in the system and they have that kind of myopic view. So, from the research you’ve done, what’s the takeaway of how informed the people within the system even understand how the global system is operating?

Jeff Snider (00:14:36):

The funny thing is, we always think scientific progress is linear. It always goes in one direction. But here’s an example of how monetary scholarship, academic scholarship about money actually move backwards. When you go back in time to do the historical research, you see there’s much more awareness, much more understanding, not the whole thing, but much more understanding about at least the basics of the Eurodollar system in contemporary time. So, back in the 1960s, for example, it took international authorities and national authorities about a decade after the Eurodollar system began to really start investigating it, because it had become that big of an issue even for national authorities like the Federal Reserve.

Jeff Snider (00:15:11):

But when they did, they were sort of putting bits and pieces of it together through… I mean, which makes sense because it’s a brand new development banks were doing things, they were not sharing the information with anybody, which is, again, why we call it shadow money. So, there was a huge, huge blind spot for even regulators and officials to try to deal with. But at that time, they did attempt to try to understand this Eurodollar system. But then they just, they stopped and they gave up, which begs the question, what is it the Fed did? What does the Fed actually do now? Which goes back to one of the initial quote that you said at the top, when I say the Fed isn’t a central bank, this is the reason why, because what happened was in the 1960s and 1970s, Federal Reserve officials, Treasury officials, government officials, officials at the BIS, or the IMF realized this monetary and banking evolution that was going on through the Eurodollar system made it almost impossible to define, let alone measure and regulate and keep on top of the monetary system.

Jeff Snider (00:16:08):

And if you’re a central bank, if you’re a legitimate central bank, whose job it is to regulate the monetary system, as we all believe, going back to Walter Bagehot in the 19th century, how do you do that when the monetary system has evolved, and it has evolved in these offshore, outside of regulation spaces that make it almost impossible for you to have much of an influence, let alone direct relationship with the banks operating there? So, what ended up happening was around the turn of the decade in the 1970s and 1980s, central bankers decided they just kind of threw up their hands and said, “Well, the monetary thing, it’s too complicated. It’s outside our jurisdiction. So, we can’t really do money anymore. Instead, we’re going to try to make it so that people believe we do money, this expectations-based policy, where we’ll communicate to the public that we’re doing something and hope that the public and banking system and business people all around the world or inside the United States will behave in ways that we want them to behave.”

Jeff Snider (00:17:02):

For example, it became commonplace that, Alan Greenspan, for example, would raise or lower the federal funds rate whenever he wanted to do something. So, if he wanted to “tighten credit” and tighten the monetary system, would he actually tighten the monetary system? Would he go into the monetary system and take money out? No, he raised the federal funds rate, which was nothing more than a signal to the economy at large and try to get the economy and try to get the markets to tighten conditions based on that signal, based on expectations. As he said, during that time, as his predecessors said before, “We just can’t keep track of the monetary system. Therefore, this is what we have left to be able to do to try to get some form of control over the economy and the marketplace.”

Jeff Snider (00:17:44):

So, it’s really about this evolution in money in banking that took place outside of their purview, which left official scrambling to try to do something else to at least attempt to maintain the role of what a central bank used to do, but it’s not a monetary role. It’s not involved in the monetary system itself. So, once that happened, monetary scholarship simply dried up. The term Eurodollar kind of disappeared, not just from internal discourse, but from public discourse as well. So, you have a wealth of scholarship up to around early 1980s and then just nothing. Because what happened was we were told, we were all told, we were taught this in school. “At that point, don’t fight the Fed, just whatever the Fed says, whatever the Fed, they must know what they’re talking about when it comes to money, you don’t need to know. Just trust Alan Greenspan and Ben Bernanke. They’ve got it all covered.” So, once there was a vibrant monetary or debate and argument, it just kind of disappeared and dried up and went away.

Trey Lockerbie (00:18:40):

But it’s not all an illusion, is it? Because if we fast forward to today, we’re seeing it happen and play out in real time, where inflation is now high again as it hasn’t been for decades and they’re raising interest rates. And now we’re starting to see things like mortgage rates go up and home prices get underwritten in a new way. We’re seeing real economic impact from these decisions or actions from the Fed. So, where does the detachment actually occur in your opinion?

Jeff Snider (00:19:05):

Well, because that isn’t actually inflation. This isn’t due to money printing. This is sort of the federal… I mean, that’s why you didn’t see consumer prices react to QE6 back in 2020. Consumer prices didn’t start to skyrocket until March and April of 2021, which was coincident to the US treasuries helicopter drops. So, this wasn’t money printing, this wasn’t the Fed creating money. This wasn’t the Fed being a central bank. It was essentially a supply shock, which was the US government redistributed borrowing through the Treasury and mostly Treasury bills actually, the US government essentially redistributing cash into the pockets of consumers. And then consumers wind up spending that cash at a time when the ability of the global economic system to supply goods and then transport goods in particular was at its lowest point. So you see inventories of goods actually crash during these periods because we had essentially a supply shock.

Jeff Snider (00:19:59):

So, it isn’t inflation as much as it was consumer prices reacting to small E economics. Whenever you have a demand curve shift out to the right, especially when supply isn’t as any elastic as it was during that time, consumer prices have to react. I know most people are saying, “Who cares? Consumer prices went way up. What does it matter if it’s inflation? Or what does it matter if we call it inflation or not?” The issue is how it ends, because if it’s nothing more than a supply shock, it’s always going to be temporary and transitory rather than something like the 1970s, where you ignite the monetary spark of excessive currency, that leads to all sorts of, well, great inflation type of problems. So, how do we tell one from the other?

Jeff Snider (00:20:40):

And one of the things that consistent with excessive currency and money printing would’ve been destruction of the US dollar has been long proclaimed, long predicted, and long forecasted. But what you see ever since last year is the US dollars exchange value going up against almost every currency, because it wasn’t money that was printed. It was simply a supply shock. And because it wasn’t money printing, the way this is likely to end is in another bad way, which is a recession. That’s really what markets have been predicting over the last more than a year, actually, because the yield curve has been flattening. So, even as interest rates have been rising, the yield curve has been flattening. The Eurodollar futures curves have been flattening. All of the signals from the monetary system itself have been sending, “Hey, there’s no money here. This is not money printing. This is a supply shock and this is going to end predictably in something like a contraction or recession.” So, it was never inflation to begin with. It was simply small E economics of a supply issue.

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But since our readership-audience for The Good Investors is wider than our subscriber base, we think sharing the reading list regularly on the blog itself can benefit even more people. The articles we share touch on a wide range of topics, including investing, business, and the world in general.

Here are the articles for the week ending 03 July 2022:

1. Why Foundational Models will Revolutionize the Future of Work and Play – Daniel Jeffries

It’s 2033 and you’re coming home from a dinner and realize your sister’s birthday is tomorrow and you forgot.

You ask your phone what’s the best gift for her and where you can get it at this late hour?

Your phone has dedicated processors for running Machine Learning (ML) models locally but it’s not powerful enough to answer that question with its small memory and slower chip speed.  But it is strong enough to ask a more powerful model in the sky.

The local model also learned a lot about what you and your family likes over time, so it packages up some key things it knows, anonymizes them, and fires off a query to a Foundational Model (FM) in the cloud via API.

In a fraction of a second, the answer comes back.

Your sister’s latest social media pics show she recently got on a serious health kick, lost weight, stopped drinking and got really into vegetarian cooking so it recommends an AirBnB cooking experience near her, with a local vegetarian chef.  It gives you two alternative experiences that are good but a bit further away and not as highly rated.  You don’t even need to go to the store and it’s the perfect present that makes you look like a hero…

…Across the world people are using cascaded FM’s networked together to do amazing work.  FMs on their own are amazing but working together they’re capable of astonishing feats and when they work with you they’re centaur units, a combination of man and machine working together to create something neither could do on their own.

Centaurs are named after Gary Kasparov’s early experiments with chess tournaments, where an AI and human teams bested pure AI and humans on their own. The tournament’s name came from the mythical beast of Greek legend that’s half horse and half man, symbolizing how man and machine can work together better…

…Biotech companies search through massive databases of proteins and chemical interactions and quickly use a fine tuned FM to design twenty potential drug candidates to fight a rare motor neuron disease that recently cropped up in South Africa.

A musician jams out a new tune and then asks the models to iterate on the chorus.  The 17th one is awesome and the musician plays it and then modifies it with a few tweaks to make it even more catchy based on a song he couldn’t get out of his head a week ago that he overheard on a radio at the local park. It goes on to be a huge hit on Soundcloud.

Materials scientists are designing new materials that make everything stronger and lighter, from skyscrapers that flex more easily to resist earthquakes, to electric bikes that are light enough to carry on your shoulder and fold up neatly to carry on the train.

Elite coders are simply telling the coding model what they want it to do and its spitting out near perfect Python code but it also recommends Go for several libraries because it will be faster and more secure.  It automatically does the translation between languages and tests it. It’s paired with an evolution through language model (ELM) coupled with a Large Language Model (LLM) and those models helps the coder create brand new, never before thought of code too, in a domain the model was never trained for by iterating on concepts quickly.

All of it is happening because of a vast global network of ambient AI models.  AI is everywhere now.  Every device is waking up and getting smarter.  We’ve industrialized intelligence and sparked a revolution in how we work, design, and play.

Welcome to the age of ambient AI…

…What are Foundation Models and why do they matter?

In essence, FMs are large models that exhibit remarkable capabilities, such as the ability to understand language, reason, create working computer code, do translations and arithmetic, understand chains of logic, generate totally new art from text prompts, and much much more.

The basic concept of FMs comes to us from Stanford University where they primarily refer to Large Language Models (LLM), like GPT-3, that are typically transformers. But the implications of FM’s go way beyond today’s architectures.  They’re a groundbreaking type of software, that’s not limited to transformers or language.

We can think of FM’s are any large and sophisticated model.  We can also think of them as a chain of cascading models that work together to do a complex task such as generate music or images or video, create mathematical proofs, design new materials or discover new drugs and more.

Many of them are already here.

GPT-3, from OpenAI, powers GitHub co-pilot that quickly writes code for developers, especially boring, repetitive code so they can focus on more creative tasks.  It’s one of the first fantastic examples of a centaur.  Originally, GitHub’s team wasn’t sure who would use it.  Would it be beginning or advanced coders?  Since its wider release to all developers, the answer is clear: advanced coders love it and use it most often.  Advanced coders are in the best position to understand when the model makes a mistake and it dramatically speeds up their day to day coding…

…In another article, called The Coming Age of Generalized AI, I highlighted researchers who were working on even more groundbreaking approaches by combining mega-models with several other key techniques.  One of techniques, called progress and compress that comes to us from DeepMind, combines three techniques, progressive neural networks, elastic weight consolidation and knowledge distillation.

The idea is simple. Create two networks, a fast learning network and a base model. That roughly mirrors the functioning of our brain yet again. Think of it as the hippocampus and neocortex. As Hannah Peterson writes in her article on catastrophic forgetting,  “In our brains, the hippocampus is responsible for “rapid learning and  acquiring new experiences” and the neocortex is tasked with “capturing  common knowledge of all observed tasks.” That dual network approach is called a progressive neural network.

The fast neural network is smaller and more agile. It learns new tasks then transfers the finalized weights to the base model. So you end up with a lot of stored neural networks good at a bunch of tasks.

But there’s a problem with basic progressive neural nets. They don’t share information bi-directionally. You train the fast network on one task and freeze those weights and transfer them to the bigger network for storage but if you train the network first on recognizing dogs, it can’t help the new network training on cats. The cat training starts from  scratch.

Progress and Compress fixes that problem by using a technique called knowledge distillation, developed by deep learning godfather Geoffrey Hinton. Basically, it  involves averaging all the weights of different neural nets together to create a single neural network. Now you can combine your dog trained model and cat trained model and each model shares knowledge bi-directionally. The new network is sometimes slightly worse or slightly better at recognizing either animal but it can do both.

It opens the door to cat-like intelligence.

A cat is a remarkable creature. It can run fast, sleep in tiny boxes, find food and water,  eat, sleep, purr, defend itself, climb trees, land on its feet from  great heights and a hundreds of other subtasks. A cat won’t learn language or suddenly start composing poetry. That’s perfectly fine because a cat is really well suited to its set of tasks; it doesn’t need  to build skyscrapers too.

Having a cat level intelligence is incredibly compelling. If you have a cleaning robot that can wash  dishes, pick up clothes, fold them, carry them from place to place and  iron shirts, that’s an incredible machine that people would clamor to buy. It doesn’t also need to write music, craft building blueprints, talk to you about your relationship problems, and fly a plane too…

…AI is a universal, general purpose technology.

The greatest breakthroughs in history are always universal technologies that affect a broad range of sectors as they branch into countless other domains and inspire unexpected breakthroughs.

Think of the printing press and the way it leveled up human knowledge across the board because now we could scale, save and replicate knowledge much faster.

Think steam engines that changed the very nature of work from human and animal powered muscle work to work done by machines.

Think of the microprocessors and computers that changed how we do art, communicate, design skyscrapers and houses, fight wars, find love, do science, make music and movies and more.

A general purpose technology like AI has direct and secondary effects on the world at large, both good and bad and everything in between.

We can think of ideas and technology as they grow and change and affect both their own domains and unexpected domains as a growing tree.  The roots are precursor ideas that eventually inspire the primary idea.  The trunk is the central breakthrough idea, which leads to a branching series of closely related ideas and some unexpected inventions in parallel domains.

2. Reducing Inflation Will Come at a Great Cost: Stagflation – Ray Dalio

More specifically, I now hear it commonly said that inflation is the big problem so the Fed needs to tighten to fight inflation, which will make things good again once it gets inflation under control. I believe this is both naïve and inconsistent with how the economic machine works. That’s because that view only focuses on inflation as the problem and it sees Fed tightening as a low-cost action that will make things better when inflation goes away, but it’s not like that. The facts are that: 1) prices rise when the amount of spending increases by more than the quantities of goods and services sold increase and 2) the way central banks fight inflation is by taking money and credit away from people and companies to reduce their spending. They also take buying power away by raising interest rates, which increases the amount of money that has to go toward paying interest and decreases the amount of money that goes toward spending. Raising interest rates also lowers spending because it lowers the value of investment assets because of the “present value effect” (which I won’t get into because it would be too much of a digression), which further lowers buying power. My main point is that while tightening reduces inflation because it results in people spending less, it doesn’t make things better because it takes buying power away. It just shifts some of the squeezing of people via inflation to squeezing them via giving them less buying power.

The only way to raise living standards over the long term is to raise productivity and central banks don’t do that…

…In summary my main points are that 1) there isn’t anything that the Fed can do to fight inflation without creating economic weakness, 2) with debt assets and liabilities as high as they are and projected to increase due to the government deficit, and the Fed also selling government debt, it is likely that private credit growth will have to contract, weakening the economy, and 3) over the long run the Fed will most likely chart a middle course that will take the form of stagflation. 

3. The Beer Game – Peter Dizikes

Thursday, August 29, 1:00 p.m.

It is a miserably muggy afternoon in Cambridge as the incoming class of the MIT Sloan School of Management—roughly 400 students from 41 countries—files into a second-floor ballroom at the Kendall Square Marriott. They are here to play the Beer Game, a Sloan orientation tradition. Unfortunately given the weather, the Beer Game does not involve drinking cool beverages…

…Rather, the Beer Game is a table game, developed in the late 1950s by digital computing pioneer and Sloan professor Jay Forrester, SM ’45. Played with pen, paper, printed plastic tablecloths, and poker chips, it simulates the supply chain of the beer industry. In so doing, it illuminates aspects of system dynamics, a signature mode of MIT thought: it illustrates the nonlinear complexities of supply chains and the way individuals are circumscribed by the systems in which they act…

…1:30 p.m.

Each Beer Game team is divided into four units of two players each, who play the roles of retailer, wholesaler, distributor, and brewer. The goal is to keep team operating costs as low as possible. We learn that teams will be penalized for having too much inventory (50 cents per case of beer per week) or unfilled back orders ($1 per case per week). Each link in the supply chain keeps track of its own costs, but a team’s score is the sum of these tallies. The lower the score, the better.

As we begin the first of 50 rounds (which represent weeks), each retailer unit draws a card indicating consumer demand for cases of beer; at the same time, all the units send slips of paper with orders up the supply chain. In response, cases of beer—represented by poker chips—move in the opposite direction, from brewer to retailer. A small number of chips are already at every station when we start.

2:15 p.m.

After 20 rounds, my team is on a hot streak.

I’m sitting at the retailer station with finance student Adah Jung, who’s been submitting orders at a level closely mimicking consumer demand. Our score at the retail station is low, and there are few chips elsewhere on the table, meaning our team’s costs are minimal. It’s hard to see how things could go wrong: with seven smart teammates and a stable supply chain, why can’t we win this thing? I can almost hear Sterman asking us to stand for a round of applause.

2:35 p.m.

Seemingly out of nowhere, our team’s distributorship has an inventory of 178 surplus cases of beer, which lasts seven weeks, adding $623 to our costs in a game where the average score after 50 weeks is $2,000 per team. How did that happen? Can’t someone tell our two teammates at the brewery just to stop making so much beer?

Well, no. “I can’t tell them anything,” observes teammate Juan Trujillo. Indeed, to simulate the incomplete information we deal with in real life, players cannot communicate across stations, apart from relaying orders. And somehow, someone on our team ordered way too much beer…

…3:30 p.m.

Sterman’s assistants tape charts to the ballroom walls detailing every team’s performance. Today’s winning score was $460 (the best possible score is about $200), while the worst-performing team racked up $6,618 in costs.

Sterman initiates a discussion, pointing out how inventories and backlogs spike and plummet erratically. The distributor on today’s last-place team went from a backlog of 70 cases to an inventory of 191 in three weeks.

One thing to learn from the Beer Game, then, is why many businesses experience boom-and-bust cycles—oil and gas exploration and housing among them. Complex systems produce nonlinear phenomena.

4:15 p.m.

Sterman pounds home a bigger lesson: our psychological habits and limited perspectives often keep us from properly understanding complex systems. To prove it, he asks distributors, wholesalers, and brewers to estimate their consumer demand; their responses are wildly inaccurate.

All too often, Sterman adds, this means we attribute problems to other people rather than to flawed systems. For instance: “I found that some people were kind of slow to take corrective action,” offers one student—who had just played for the winning team, a fact Sterman emphasizes to much hilarity.

It doesn’t make sense for us retailers to blame our teammates—who had imperfect information—for our disappointing scores. “It just cannot be true that, by chance, all the smart people ended up as retailers and all of the people running the factories were dumb,” Sterman says. The Beer Game’s structure makes it hard for certain players to perform well. It’s not the people; it’s the system.

Thus, firing people tends to be a futile management action. “Your role as a leader is to create a system in which everybody can thrive,” he says…

4. Why does the Stock Market go up? – Eugene Ng

A Google Search of “Why does the Stock Market go up?”, and Investopedia gives you up a broad range of factors.

The factors range from the supply and demand of buyers and sellers, to economic indicators, consumer confidence, wars/politics, concerns over inflation / deflation, government fiscal / monetary policy, technological changes, natural disasters or weather events, corporate or government performance data, regulation/deregulation, and the level of trust in the financial sector and legal system, amongst so many others.

But this doesn’t really answer the question, doesn’t it? It only leaves you, more confused, and begging for a better answer…

…The factors listed above are not wrong. Yet, they do not help you figure out why stock prices rise.

In the short-term, stocks will move up and down for a variety of random reasons — all of which does nothing to increase your chances of a positive return.

Thus a better question would be:

“Since the short-term does not really matter as much, why then does the stock market go up over the long-term?”

To get closer to the truth, you need to understand the components which drive the returns on your stock investment.

The Total Shareholder Return (TSR) from holding common publicly-traded stocks can be broken down into three key components: (1) growth in Earnings per Share (EPS), (2) change in the Price-to-Earning (PE) valuation multiples, and (3) earnings from dividends…

…With S&P Global providing us with historical data on the S&P 500’s closing levels, Sales per Share (SPS), Earnings per Share (EPS) and Dividend per Share (DPS), they provide clues on what the growth has been thus far…

…Take 2021 to 2003, the longest period spanning over 18 years (first row, last 5 columns from the right). During this time, the S&P 500 Index more than quadrupled from 1,112 to 4,766, with TSR* growing by ~4.3X (8.2% CAGR).

The contribution of the Earnings per Share (EPS) growth is telling. Earnings per Share (EPS) grew by ~4.1X (8.1% CAGR) from 48.7 to 197.9. Further breaking down that EPS growth, Sales per Share (SPS) grew by ~2.2X (4.5% CAGR) and Net Income Margin Growth (NIM) grew by ~1.8X (3.5% CAGR).

Thus the growth in earnings (EPS) accounted for the majority (~95%) of the TSR* growth, with growth in sales/revenues (SPS) and improvement in net income profit margins (NIM) accounting for ~52% and ~43% of TSR* growth respectively…

…Given what we have laid out so far, you, you should not be surprised to learn that over the long-term, it is earnings growth, supported by revenue and profit growth, that drives the stock market higher, and to a much lesser extent, valuation multiples.

5. Pioneer Helped Turn Her Family Store Into Japan’s Biggest Retailer – Chieko Tsuneoka

First her father died young, then her mother, then her older sister. At 23, Chizuko Okada inherited the job of running her family’s clothing store in Mie prefecture, Japan.

It was 1939, and war with America was just around the corner. Few could have foreseen that the little business would develop into Japan’s largest retailer by sales—or that a woman would be its driving force.

By the time Chizuko Kojima—her married name—died on May 20 of old age at 106, the company now known as Aeon Co. had thousands of stores around Japan and the rest of Asia and annual revenue equivalent to $64 billion…

…Ms. Kojima was born on March 3, 1916, as the second daughter of the Okada family, which had run a fabric and kimono store since 1758 in Mie prefecture, just west of Nagoya in central Japan.

Chizuko’s father, Soichiro Okada, modernized the business but died of heart disease in 1927 at age 43. Then Japan was hit by the Great Depression, which caused bankruptcies and joblessness.

In a 2003 book, Chizuko wrote that she believed it was necessary to be ready for such cataclysms by studying history. The hard times deprived her of a chance to pursue higher education in Tokyo.

After taking over the family business, Chizuko managed to keep it going during World War II until a U.S. bombing raid destroyed much of their home city of Yokkaichi in June 1945, including the Okada store’s stock.

At the time, customers held coupons similar to gift certificates entitling them to store goods. The store no longer had anything to offer, but Chizuko posted notices throughout the city saying her shop would give cash in exchange for the coupons, recalled her younger brother, Takuya, in a 2005 autobiography. It was a way of maintaining customers’ loyalty that would pay ample dividends in years to come.

Chizuko loved studying and during the war, she read a book about Germany’s inflation after it lost World War I. When Japan surrendered in World War II in August 1945, she predicted the same would happen. She gathered her cash and bank loans and bought as much merchandise as possible, reopening the shop in March 1946, ahead of an inflationary surge that hurt other businesses.

“All the merchandise flew off the shelves,” Takuya recalled.

Chizuko wrote of the episode, “Through my own experience, I learned the importance of studying and reading records of the past.”…

…In 1959, when the Okada family business still had just two stores, she came back to take charge of personnel and other behind-the-scenes management issues.

That year, Chizuko and Takuya made their first visit to the U.S. and toured the famous Sears, Roebuck and Co. store in Chicago. Takuya wrote that he was impressed by the giant scale of the business. Paging through the thick Sears catalog full of pictures of refrigerators, washing machines, clothing and a myriad of other goods, he imagined the day that Japan, too, would enjoy that kind of affluent life.

Chizuko was impressed by the Sears pension system, thinking it would create a loyal workforce. She introduced one a decade later, as her brother rapidly expanded the retailer through mergers. She also introduced an in-house training organization, today known as the Aeon Business School…

…Chizuko was one of the first managers in Japan who aggressively hired female full-time employees and homemakers as part-timers. She saw that many women worked in the U.S. and believed Japan should follow suit.

By having women at the company, “we were able to bring on board the viewpoint of the customer—how much to sell and at what price,” she said in a television interview when she was 90.

6. Make Haste Slowly – Chris Mayer

I had been reading The Art of Worldly Wisdom: A Pocket Oracle, a book written in 1647 by Baltasar Gracian, who was a witty Jesuit from Spain. His book of 300 aphorisms, with  his commentary on them, has been translated into many languages and has earned the praise of many philosophers ever since.

Arthur Schopenhauer loved it so much that he prepared a German translation himself. Schopenhauer said it was particularly good for young people, as it would give them experience it would otherwise take years to obtain. “To read it through once,” he wrote, “is obviously not enough; it is a book made for constant use.”…

…Anyway, there is a passage where Gracian talks about the motto “festina lente.” This Latin phrase is usually translated as “make haste slowly.” One must be very patient and yet ready to act swiftly. And the fastest way to achieve your goals is sometimes by doing nothing.

The motto was a favorite of the Roman Emperor Augustus. Engravers captured the idea with an emblem of a dolphin wrapped around an anchor, which they stamped on coins. Another emblem captured the same idea with a crab and a butterfly; again marrying this idea of fast and slow.

Festine lente recurs throughout history and has been captured in a variety of images, such as a rabbit coming out of a snail shell. The Medicis chose it as their motto and illustrated it with a sail-backed tortoise.

I thought the idea beautifully captured an important idea in investing that is often counterintuitive: to get where you want to go the fastest often means acting very slowly if at all…

…It does seem incredibly counterintuitive to say, “No, you shouldn’t  try to sell before a recession.” Or: “No, you shouldn’t ‘reposition’ your portfolio based on recent events.”  Don’t these seem like logical things to do?

Not if you want to enjoy the wonderful effects of compounding capital over long periods of time. The main problem with trying to do the above is they are too hard to do well enough. You have to think about trying to do these things repeatedly over a lifetime of investing. The odds against you are very great. Sure, you may be right sometimes. But you will most certainly sit out stretches of time where you could have earned great returns because you’re afraid of a recession. Odds are you won’t get those “repositionings” right repeatedly either.

7. How Parents’ Trauma Leaves Biological Traces in Children – Rachel Yehuda

After the twin towers of the World Trade Center collapsed on September 11, 2001, in a haze of horror and smoke, clinicians at the Icahn School of Medicine at Mount Sinai in Manhattan offered to check anyone who’d been in the area for exposure to toxins. Among those who came in for evaluation were 187 pregnant women. Many were in shock, and a colleague asked if I could help diagnose and monitor them. They were at risk of developing post-traumatic stress disorder, or PTSD—experiencing flashbacks, nightmares, emotional numbness or other psychiatric symptoms for years afterward. And were the fetuses at risk?

My trauma research team quickly trained health professionals to evaluate and, if needed, treat the women. We monitored them through their pregnancies and beyond. When the babies were born, they were smaller than usual—the first sign that the trauma of the World Trade Center attack had reached the womb. Nine months later we examined 38 women and their infants when they came in for a wellness visit. Psychological evaluations revealed that many of the mothers had developed PTSD. And those with PTSD had unusually low levels of the stress-related hormone cortisol, a feature that researchers were coming to associate with the disorder.

Surprisingly and disturbingly, the saliva of the nine-month-old babies of the women with PTSD also showed low cortisol. The effect was most prominent in babies whose mothers had been in their third trimester on that fateful day. Just a year earlier a team I led had reported low cortisol levels in adult children of Holocaust survivors, but we’d assumed that it had something to do with being raised by parents who were suffering from the long-term emotional consequences of severe trauma. Now it looked like trauma leaves a trace in offspring even before they are born.

In the decades since, research by my group and others has confirmed that adverse experiences may influence the next generation through multiple pathways. The most apparent route runs through parental behavior, but influences during gestation and even changes in eggs and sperm may also play a role. And all these channels seem to involve epigenetics: alterations in the way that genes function. Epigenetics potentially explains why effects of trauma may endure long after the immediate threat is gone, and it is also implicated in the diverse pathways by which trauma is transmitted to future generations.

The implications of these findings may seem dire, suggesting that parental trauma predisposes offspring to be vulnerable to mental health conditions. But there is some evidence that the epigenetic response may serve as an adaptation that might help the children of traumatized parents cope with similar adversities. Or could both possible outcomes be true?..

…It is tempting to interpret epigenetic inheritance as a story of how trauma results in permanent damage. Epigenetic influences might nonetheless represent the body’s attempts to prepare offspring for challenges similar to those encountered by their parents. As circumstances change, however, the benefits conferred by such alterations may wane or even result in the emergence of novel vulnerabilities. Thus, the survival advantage of this form of intergenerational transmission depends in large part on the environment encountered by the offspring themselves.

Moreover, some of these stress-related and intergenerational changes may be reversible. Several years ago we discovered that combat veterans with PTSD who benefited from cognitive-behavioral psychotherapy showed treatment-induced changes in FKBP5 methylation. The finding confirmed that healing is also reflected in epigenetic change. And Dias and Ressler reconditioned their mice to lose their fear of cherry blossoms; the offspring conceived after this “treatment” did not have the cherry blossom epigenetic alteration, nor did they fear the scent. Preliminary as they are, such findings represent an important frontier in psychiatry and may suggest new avenues for treatment.

The hope is that as we learn more about the ways catastrophic experiences have shaped both those who lived through those horrors and their descendants, we will become better equipped to deal with dangers now and in the future, facing them with resolution and resilience.

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Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. Of all the companies mentioned, we currently have a vested interest in Alphabet (parent of Google) and Wix. Holdings are subject to change at any time.

We’ve constantly been sharing a list of our recent reads in our weekly emails for The Good Investors.

Do subscribe for our weekly updates through the orange box in the blog (it’s on the side if you’re using a computer, and all the way at the bottom if you’re using mobile) – it’s free!

But since our readership-audience for The Good Investors is wider than our subscriber base, we think sharing the reading list regularly on the blog itself can benefit even more people. The articles we share touch on a wide range of topics, including investing, business, and the world in general.

Here are the articles for the week ending 26 June 2022:

1. Josh Wolfe, Chris Power – Factories of the Future – Patrick O’Shaughnessy, Josh Wolfe, and Chris Power

[00:02:39] Patrick: Chris and Josh, this is going to be a totally different conversation about an area that I don’t think I’ve ever explored before, very keyed in on a certain kind of manufacturing. I’m sure we’ll hit bigger themes of onshoring of manufacturing in just the next generation of this part of the economy. We’ll spend a lot of time around precision parts, what Hadrian’s doing, why Lux is interested in this area, what Chris, you and your team, are building. To set the stage, Chris, it would be great if you could, as you did for me on the phone recently, give an overview of the recent past and what has happened in this world. It’s become a topic that everyone’s talking about a little bit, but probably doesn’t really fully understand the recent intermediate past of manufacturing, where it happens, why it’s happened that way. So a little bit of a history lesson would be a great place to start to frame our conversation.

[00:03:23] Chris: For advanced manufacturing, in general, which I describe as space, defense, semiconductor, eVTOL, energy, medical devices, basically everything in the Jetson’s flying car, future, all has to be domestically manufactured because of ALTAI requirements. It’s super high precision components. And basically, 80% of the manufacturing parts for those industries flows through a high precision network of machine shops. There’s 3,000 or 4,000 of them. Average size is 10 to 12 million in revenue. In aggregate, they do 40-50 billion in revenue, but it’s incredibly fragmented, super low NPS. It’s the most perfect Keith Rabois fragmented, low NPS, vertically integrated structure you could ever possibly think of. Historically, what happened is this was built off the defense primes needing a bunch of suppliers. All these machine shops got built in the first Space Race or the Cold War. They were businesses that got started 30 years ago by 30 year olds. And now they are 30-year old businesses run by 60 year olds. What’s happened in the last five years is there’s not a lot of slack in the system. And generally, a machine shop might be making some semiconductor parts, some parts for Boeing, and then some parts for Raytheon, for example. In the last five years, because of the boom in commercial space, which has been largely driven by lowered launch costs, the success of companies like SpaceX and Anduril, and then investors like Josh have been putting money into satellite companies, rocket companies, the whole thing. If the top level, you’ve got a bunch of net new spend in high precision components from commercial space and companies like Anduril that are flooding the same supply chain. That’s big problem number one.

And what you’re seeing for those customers is, “Hey, I’m trying to ship a satellite really quickly. I’m getting parts in 6 to 10 weeks. That’s insane. Because I’ve got an aerospace engineer sitting around for another part, wasting time, when I’m trying to get a launch up. I’m trying to get my startup goals.” So all these new entrants to the market are going way, way faster than your traditional primes. Now, that’s putting speed pressure on the supply chain. And basically, you’ve got this thing where customers want fast supply chain, huge opportunity to build a business meeting that need, with a bunch of net new spend in the supply chain. That’s phase one is Hadrian builds a better mouse trap for new space and new defense. The second phase, which is really scary for the country though, is all of those 60 year olds are going to retire in the next 5 to 10 years at an increasing rate. And 90% of them, historically, when they do retire, don’t transition to private equity, or sell or transition the business to a son or a daughter. They sunset the business, lock the door, sell a machine, and throw away the keys. There’s two bits that are really dangerous for the country about that. The first one is just purely capacity. In the decade where we’re trying to butt heads with the CCP and win Space Race 2, the capacity that feeds rocket satellite, drone companies is going to fall through the floor because of this capacity issue. They’re retiring, so you’ve got this huge supply and demand imbalance in the worst possible decade that that could be happening. And on top of that, it’s not as simple as, say, a Raytheon going, “Hey, Patrick’s Machine Shop, you’re retiring. Let’s take all the digital files that tell someone how to make those parts, and give it to another machine shop.” Most of them have been made for 20 years. There’s no CAD file. The drawing is in someone’s desk drawer. And we’ve just seen this where we shipped something like a third of all our Stinger and Javelin missiles to the Ukraine. This is on the defense side, but this happens across space semiconductor really. So we shipped all over to the Ukraine. The Biden administration went to Raytheon and said, “Hey, we need more Stingers and Javelins.

And then Raytheon came back and said, “Well, apart from the fact that supply chain’s super bottlenecked and we can’t ramp up production, we just don’t know how to make any of the parts anymore. And it might take a couple of years to figure it out.” So it’s a complete disaster, both on net new spends in secular growth and decline. But what people don’t realize is it’s not as simple as, “Hey, let’s raise your semiconductor. Let’s throw a 50 billion into an Intel plant in Arizona.” Because in the ’70s and ’80s, when we outsourced advanced manufacturing, what we lost was not just capacity or capability, it was the talent and the people. And what people don’t understand about manufacturing, it’s like software engineering. To get AI researchers, you have to have a base of backend software engineers. You’ve got a million software engineers, and it breeds the best. It breeds the best. And all of a sudden, you’ve got some top tier people in deep cloning and all that other stuff. It’s the same in manufacturing. You can’t really skip these training levels. So what we lost was not the knowhow to do a specific part, but the talent base that can produce better and better people that can work on things like semiconductors or advanced manufacturing. The slack in the system is not simply a capital problem. It’s this talent based problem. You can solve some of that by trying to grab some people from Taiwan, people who really know this, and rebuild all these industries. But it’s much, much slower than people think it is because it’s not as simple as turning a capital key, buying some machines, and ramping up production capacity. It’s incredibly difficult to do. It’s a huge commercial opportunity, but it’s incredibly important that we get this right for the country because space is basically a defense domain. Peace through strength is a huge deal. We’ve created this period of peace with Pax Americana. And I think in the next couple of years, maybe in the next 18 months, we’re going to really see that a lot of that is risked, and there’s going to be a huge wake up call when the average American consumer not just can’t buy an iPhone for less than $4,000, possibly can’t buy one at all because of all this global shifting of the advanced manufacturing supply chain…

...[00:12:57] Patrick: I actually just did one of these with Brian from Anduril. And it was really interesting to dive into the nature of the pieces of what they’re building and their goal for speed, simplicity, modularity the philosophy of how these things are built, whether it’s Ghost or whatever the product is at Anduril, is it’s very different from a Predator drone or something that Lockheed or Northrop would put out over the course of a decade. I’m curious, Chris, how much you think in the success case for Hadrian, where it’s everything you dreamed of and more, 10 years from now, how its existence changes the nature of the things that get built? What will this new manufacturing capability, just like Stripe and Twilio, people build stuff that they couldn’t have dreamed of before because they were able to go so fast with this new tooling, how do you think about that, Chris, in terms of what this might lead to? That even though there’s amazing things happening at SpaceX and everywhere else, it’s on the back of these 3,000 mom-and-pops. What will be different in the success case for Hadrian, for the people at the top of the chain?

[00:13:53] Chris: I think if you think about software engineering 10 years ago, maybe to start a SaaS company, it costs a million dollars, and you were spending more than 50% of your time on activity like running a server farm or building payments that every single software company had to deal with. When you see these platform infrastructure companies like AWS come out, or Stripe come out, or Twilio, you get to really interesting dynamics. One of which is the cost to start a company in this space goes through the floor. So now with all the tooling, you can start a software company for a couple hundred dollars. Secondly, the number of companies that get started because of that tooling goes through the roof. And then the third thing is the speed at which those companies can iterate, basically turn engineering time into a good product that the market wants, goes through the roof because their iteration cycle goes through the roof.

If we get this right, we should be able to drive three things. One is that existing companies can iterate on products in order of magnitude faster, which means that at the product layer, you just get better products. You’re not doing a year long cycle for a satellite. You’re doing a two month cycle for a satellite. As you’re getting feedback from the customer, your designs can change way, way faster. Secondly, by having Hadrian as a platform, we should be able to dramatically lower the cost of starting advanced manufacturing companies, which will drive a Cambrian explosion in both this evolutionary, who’s winning in the marketplace to build a satellite company or a drone company. The raw number of these companies that start will go through the roof. And that would be success for me.

[00:15:19] Josh: One of the things you just said, which I think is really interesting, there’s this old thought experiment, which was actually manifest in a physical experiment, where you took two different classrooms of people that were making some sort of pottery. They had a very specific end state of a pot that they had to make. And one was told, “Spend two hours or an hour or whatever it was making the pot as perfect as you can.” And the other was told, “Make as many pots as you can.” The latter, which was rapidly iterating, and trial and error, and trial and error, ended up making the more perfect pot. So that idea, which I think applies to industry, is if you make something and then you’re waiting forever to test it in the real world versus being able to rapidly iterate. The latter example is something that Hadrian’s going to enable, that in turn then, lets many more startups flourish for less capital. We can do more experiments, fund more companies. They can fail fast. Or they can come up with a product that is superior and competitive, and then build a platform from there…

…[00:22:50] Patrick: Chris, can you help us understand, going all the way to the beginning of the supply chain, the rare earth or base metal component of this process? Because I don’t think people have probably thought too much about is this aluminum, is it steel, is it titanium, is it something else? What’s the 101 on the actual raw materials that are important in this process? Because out of nowhere, after a decade of silence, the commodity world has come alive. There’s issues shipping, there’s issues sourcing, there’s issues in pricing, there’s inflation. This becomes a really important thing really quickly. So give us a little tutorial on what are the important metals that go into all of these shops as raw material and anything that you think we should know about the nature of that today?

[00:23:30] Chris: Basically there are four main alloys that all space defense semiconductor satellite companies use. Aluminum, 6061, 7070, steel variants, so 306, 316, 30X, titanium, and then Inconel variants. There’s a ton of aluminum on satellites. There’s slightly less aluminum on rockets. And then on rockets, you start to get into steels and harder metals like titanium and inconel because the closer you get to the engine, the hotter it is, so you need material that can withstand heat. And then it’s the same thing for the defense side. So if you look at a fighter jet, there’s a bunch of structural aluminum, there’s a bunch of structural titanium, because it’s incredibly lightweight, and then the engine is incredibly hard, heat withstanding materials like inconel. Those are the input materials to the parts. So let’s talk about that. I mean, let’s talk about the parts that are on the machines that we run in our factory, because that’s a little bit scarier. In a sense, I think the aluminum price over the last year, it’s come down a little bit now, but I think it doubled. That was a supply chain shock from the inputs, but then the mills themselves in America had a labor shortage. There was just lack of supply, so the price went up. The parts that went on those satellites during that nine month period, the machine shop can’t absorb them, so it gets passed on, so the satellites are now 30% more expensive. And if you look at where those materials get sourced from, we have a pretty good supply of aluminum in the United States. 90% of the titanium in the world comes from Russia and the Ukraine. A bunch of aluminum and steel comes from Europe as well. And actually, if you look back to the Cold War, our spy planes were made out of mostly titanium. Skunkworks had to sneak titanium out of Russia to be able to make spy planes.

This is why I think a lot of the hand waving around sanctions is ridiculous, because if you’re in an adversarial position and you say, “Hey, we’re doing all these sanctions,” and then there’s 50 exclusions because you’re kidding yourself about the fact that this guy’s the only one with a titanium. It’s just ridiculous. That is a real problem both on lack of production, but also availability of supply to American companies because a lot of that is offshore, which makes me crazy, because the State Department years ago should have been going to Latin America and Africa and getting supply of all this stuff and partnering with these countries and raising them up. Whereas China through Belt and Road has secured a lot of this global supply because they’ve got Russia locked up with the whole energy pipeline thing. They’ve got Africa locked up. So it’s a real huge challenge. For rare earth materials, which are more things that go into batteries, chips, that sort of stuff, they’re not on the parts that we’re producing, but our machines obviously have a ton of chips in them. And then every single satellite or rocket has a bunch of chips or circuit boards in them. So that’s a huge problem, and that’s way more strategic because obviously 70% of the world’s chips come from Taiwan, most of which is TSMC. And then the other thing is the rare earth minerals like lithium or cobalt are largely Latin American, but the Latin American mines are much, much less developed. That’s a huge challenge as well…

…[00:27:54] Patrick: Chris, if we zoom all the way back to the unit here of the mom and pop machine shop, what are the key set of jobs being done by one of those given shops? You mentioned the nature of them. It’s the 60 year old soon to retire making 10 or $12 million revenue per shop or something like this. What are the key components that are inside each of those shops that you want to lift out as core functions or jobs to be done and then start innovating on inside of a Hadrian factory?

[00:28:20] Chris: There’s three big chunks. One is the digital side of manufacturing, which is taking a customer PDF print and doing a bunch of creative geometry work to get that into machine code that tells the machine how to cut the part. That’s one big chunk, and that’s very software automation heavy. The second chunk is running the machine itself, which again is less of a robotics problem. It’s more of a software engineering problem. So what a master machinist does on the control is a lot of manipulating code on the fly as they respond to slight differences in the cutting tools, slight differences in the raw material. That’s a big operations software problem. And then the third layer is general logistics. So you’ve got unpredictable cycle times of each operation in the factory, you’ve got huge variances in how long something takes to inspect from one part to the other. And then you’ve got a lot of customer requirements that are incredibly variable for each purchase order that comes through. And as an example, this is a very simple example, but can create a lot of operational noise if you don’t get it really right at the top end of the funnel is laser marking a part. So producing all these space components and then at some point an engineer’s going to want to test it, often there’s a call out on the print that says, “Hey, engrave this part with a serial number, the purchase order number, or the print revision that the aerospace engineer said, ‘This is my part number.'”

You might think that’s easy, but there are about 10 aerospace known specifications of the depth of the laser engraving, how big or small it has to be, and what the function of that is. In a regular machine shop, you might have a guy running a laser machine that’s staring at a PDF print that might remember the specification. So a lot of it is that documentation of that engineering knowledge and then systemizing it so that the whole thing flows smoothly, yet haven’t got a bunch of random art going on. Even load balancing that is an insane challenge because you might have all these machines set up for making the part, inspecting the part, cleaning the part. But if you get one of those throughput messages wrong, where you’ve got, say, 10 jobs running through a facility at once, but they all happen to hit the quality inspection station at the exact same time, all of a sudden you’ve got a bottleneck and all the jobs are late. Before you tell the customer, “Hey, we can get this in two weeks,” having that load balancing like a data center with foreknowledge of where the capacity bottlenecks might be in three weeks so you can make good judgment calls on what you’re promising versus what you’re delivering is a huge data science and operational excellence challenge…

…[00:32:09] Patrick: Josh, how do you prosecute diligence for something like this as an investor. Hearing all that, I’m going to ask more follow up questions in the minute on the unit of the machine and the areas of innovation and the machinists, et cetera. But when you’re facing something like this early on and it is factory one or factory one’s still a glint in your eye, how do you do diligence on someone’s ability or a team’s ability to execute something like this?

[00:32:33] Josh: I’m probably going to get myself in trouble with this. First you make an investment in another company that fails.

[00:32:38] Patrick: Good start.

[00:32:39] Josh: And that’s what we did. We and Founders Fund actually were co investors in a company that did not work. Part of that was narrow focus, part of that was team and structure. There was something proverbially different with Chris that their light shone brighter. As you can hear him talk, not only understanding the macro, but if you have a customer like Brian, Andrew, or Palmer, they want to anodize titanium and aluminum and parts that not only have electric chromatic coatings that strengthen and provide performance, but look really cool. They’re super high demanding, yeah. So Chris’s understanding of the macro to the micro is something that was inspiring, but we were still making a bet without any existence proof of why we were basically going to make a very similar investment as we had before, but this time was different, those dangerous words. And it truly came down to his vision of understanding industry structure, his vision of seeing the technological pieces that could be put together. Notably, he told us why we lost money in our last investment, which was super valuable. So he diligenced our failure to diligence properly our prior investment. And part of that was you don’t want to automate everything.

He’s like, “You don’t want a 100% automation. You need humans in the loop in some of these aspects.” Maybe you want 80/20 or 70/30, but you need people that are there able to very quickly look at the geometry of a part or design, make a human decision, let the computer do it. A lot of it really came down to Chris understanding the macro, the micro of individual parts, the flow, where bottlenecks were. And then I think this is really important, you really have two different cultures. You have a machining culture, which is very blue collar in many cases. It is people working with their hands and really deep narrow specialists. And then you have this coding software culture, which is almost the antithesis of that. It would be good to actually hear from Chris, how do you think about those two people speaking very different languages, sometimes growing up and going to very different schools, actually being teammates and working with each other, because that answer that we got from him was super confidence inspiring.

[00:34:29] Chris: The difficult thing, going back to previous failures in his space, was both in private equity and software engineering trying to automate manufacturing, the previous approaches have been very egotistical in the sense of, “We don’t need any industry knowledge.” Either I’m a guy with a spreadsheet and I know how to do an IRR calculation. Operations doesn’t generate profit, finance does. So the attitude towards machinists or manufacturing people is very downwards looking. I saw the same arrogance in Silicon Valley, which was, “Let’s not try and work with the best in the industry to automate this in the right way.” It’s, “Let’s grab 30 PhDs and don’t hire a machinist until employee number 28 and just try and figure it out ourselves,” which for me is just this very coastal elite looks down on flyover state dynamic. What I recognized was in machining, all of the problems have been solved by people, the knowledge is in a bunch of people’s brains. It’s not like we’re inventing a new algorithm for machining. If we did nothing, but just find all the right answers and get them into software and process, we would win immediately. To do that you have to create a culture where people feel comfortable working with a software engineer and machinist and an operations person all in one conversation and setting the standard that just because you’ve got a maths degree from Yale and this guy didn’t graduate high school, setting that culture so they work collaboratively and there’s no finger pointing or whatever and everyone’s pulling in the same direction is really, really important. This was one of the most important things that I worked on, and it’s a combination of making sure, even really simple things like no matter of whether you’re a machinist or a software engineer, your equity that you get in Hadrian is the same based on your rank. The pay is the same. All this other stuff is super, super, super important.

Really finding the people from industry that want to share their knowledge and want to train people, is an incredibly rare thing. So we’re incredibly lucky to have the 20 or 30 people in industry that actually want to share their knowledge and understand that we’re all pulling in the same direction. And that is a really unique thing. To give you an example of how scary this is, even at the most innovative space companies, to train someone on how to inspect a part is usually this thing of like, “Hey, we’ve got all these amazing people that want to work in manufacturing but I’m on X number of dollars per hour, and I don’t want to share my knowledge because my job is at risk.” Even something as simple as training new entry to the workforce is incredibly hard because of this protectionism. People ask me what the secret sauce is, and I think investors think we invented this new technology and that’s the core of the company. The core of the company is 50 people soon to be 80 and 100 pulling in the same direction. Understanding that what we’re building is a culture of, here’s a problem let’s solve it and no matter where the solution is coming from, implement it and work together. That’s the core of what we’re building which long term is going to be a huge, huge advantage. Because if we get Hadrian right, there’s no reason why we can’t take the same team and go solve tube bending or raw material or whatever it happens to be.

[00:37:14] Patrick: You described this the first time we talked as the PhD arrogance trap, which I really liked as a phrase. Thinking you can just solve every single problem immediately with technology. Interesting to hear about the inner relationship between the two teams or the two modalities. When it comes to the individual machine and the machinist working together inside of a Hadrian factory, again maybe starting to squint a little bit and look out 2, 3, 4, 5 years, what do you think the innovation zones are on the machine side specifically? In what ways will a Hadrian machine be better five years from now than it is today? Because it sounds like there hasn’t really been much innovation on the machines themselves in these mom and pop shops?

[00:37:51] Chris: Actually, I think that’s slightly incorrect. But I would say that we are not really innovating on the machines themselves. And that’s part of the trick here is we are buying everything mostly off the shelf and then doing really tight software integrations to override the core software that lives on these machines to make them run better. But we’re not doing mechatronics and upgrading the machines themselves. Building your own machines while trying to scale a factory is like two impossible tasks. What we’re really doing is going, “Hey, these machines have APIs that control everything about them.” No one’s ever used an API for this machine ever before, and that’s really where the technology curve is honestly. Even down to simple things like, you’re meant to be able to run a machine overnight without it stopping itself. There’s actually 20 or 30 reasons why a machine would stop itself running. A Tool breaks, something goes wrong in the controller, it’s like a literal software bug. A lot of our automation is actually building the robustness into these vendor machines so that they self-correct overnight so we can get the throughput and the efficiency.

One of the reasons why you have a second shift at a machine shop, which is incredibly inefficient, is because someone’s hanging around waiting for the machine to error out and they know how to clear the error and get it going again. Which sounds insane, but that’s honestly 70% to 80% of the problem. It’s hilarious having people from industry where we come back in the morning and the machines run itself overnight and there’s 10 good parts sitting there. And people are like, “Wow, this is amazing.” I’m like, “What do you mean? These machines are designed to run overnight?” And they’re like, “No, well, it almost never happens in reality.” The reason is, because over the last 5 to 10 years, the amount of software that’s in these machines has grown exponentially, but no customer of the machines has ever been able to take advantage of it because, what machinists knows how to write software? What machine shop can afford to pour a software engineer into the problem? Or even if they had a software engineer, have them spent three months of R&D on figuring all this stuff out versus just firefighting operations because they’re trying to deliver for a customer. So that’s more what’s going on than us innovating on the hardware side.

[00:39:44] Patrick: And the innovation, the units of innovation themselves driven by software, is better-cheaper-faster the right way to think about what you hope to accomplish by starting to tune the dials using software?

[00:39:55] Chris: Definitely on the front end of the factory in the digital manufacturing CAD and CAM programming space, 100%. Because you just want to turn a 20 hour process into a 2 hour process. It’s possible. It should be done. We’re chipping away at the marble and will get there. For the factory, I actually think that simplification and robustness are the two most important things, because in manufacturing, complexity and lack of robustness are what drives costs. You’re actually better off having a system that works every single time that’s simple. That gives you two things. One is, there’s less errors so there’s not a bunch of people firefighting. And because it’s simple, you can train many, many more people into that system. Getting rid of a lot of the complexity of making everything truly error proof is a lot of the innovation there, which seems counterintuitive. But in the real world, you want as little errors as humanly possible versus trying to dial up the efficiency on something so high that it breaks one in even every 10 times and all of a sudden you’ve got three or four people standing around figuring out how to solve the problem. That’s really, really what’s important there.

Now, what you get from that is speed. So speed is not necessarily like, cut the part faster. It’s at every handover point, don’t have to go back in the step, go back in the step or have this station hanging around waiting for information because you’ve got errors. So the whole factory speed is optimized by having each of these individual pieces incredibly robust. For the customer layer, they get speed. What’s great about speed is everyone wants it, so we also get pricing power. As we hit the robustness layer, we have margin efficiency growth because people are hitting things every single time cleanly versus running around scrambling like, where’s this bit of paper, where’s this tool? Now on the customer layer because we are reliable and fast, we have enormous pricing power. It’s this interesting dynamic about manufacturing where, if you just focus on robustness and cleanness of the process, you kind of generate margin improvement automatically and therefore you get pricing power because you’re fast and you reliable.

2. Quotes from Seth Klarman Interview – The Transcript and Seth Klarman

2. The impact of rising rates: 

“That is going to test financial institutions who’s been writing derivatives they shouldn’t write, who’s been stepping out to take greater risks in their portfolio because if you can’t make it in bonds, people try to make it somewhere else.”

3. Watch out for anchoring

“After you buy something you paid for, it doesn’t matter. People cling to the idea that at least they should get their money back; maybe there is bad news, and you should sell before it goes lower; maybe put it into something else where you get your money back, but people prefer to make it back where they lost it. People anchor numbers in their heads, and they hold on to them. They have a way of remembering what happened relatively recently. If you recently had a pandemic, you over-worry about the next pandemic even though they don’t happen that often. I was certainly guilty of that after 9/11 myself. It seemed obvious that we’d get hit again, and then we didn’t for a long time.

4. Best business book: 

“We should not expect people to be rational all the time. Daniel Kahneman does a beautiful job in Thinking Fast and Slow. It is in many ways the best business book, the best investing book ever written even though it’s not ostensibly about business or investing because it tells us about ourselves”…

...6. On finding edge: 

“There are lots of ways to develop edge as an investor. One of the ways is deep fundamental knowledge. I have total respect for people who dig incredibly deep in an area where they’re doctors and medical researchers. They study biotechs and that’s formidable. No one should underestimate that power, but that’s not the only kind of inefficiency, as the inefficiency might be informational. Two things happen in markets; right markets are inefficient partly because of human nature, as I mentioned; greed and fear. People get greedy and panic; in some cases, the panic is legitimate. “Oh crap, I leveraged my portfolio, and I’m getting a margin call.” or “I have short-term clients, and they can redeem, and I’m getting redeemed, and I have to sell whether I like it or not.” There are other constraints on investors that also create inefficiencies.

Once in a while, we get a call from someone with one asset in their private equity fund who want to raise the next fund. They want to book a gain on that asset. And so, call it the last asset phenomenon. People literally will sell that more urgently, and maybe they’ll favor getting it done over the exact price they get because they want to raise their next fund and move on. They want to book a game and get paid. We live in an imperfect world, and their clients should probably not love that, but maybe their clients would love it. The manager has a lot of things to balance, so that’s just one little example. When a bond gets downgraded, there’s always an immediate rush to the exits by the investment-grade holders. A bond gets downgraded to junk, say when the bond goes literally from BBB to BB. Many bonds have to get sold; some are probably sold in advance. It’s good to know what a company does, its operations, and its worth. It’s also interesting to know that there’s a very large seller, and the bonds are 20 points lower. With essentially no change in any information, just the rating of a 26 year old at moody’s. So those are the kinds of things that can trigger our interest then we do fundamental work”…

…10. On making mistakes:

“Today, there’s not so much mean reversion. Things may not be mean-reverting because of technological disruption, so I think investors have had to raise their game massively in the last several decades, and I’m not done raising it. I probably haven’t raised it as high as it needs to be. It is a great time to be knowledgeable about technology; it was a great time if you could figure out what Amazon was up to. For a value investor, it looked hopelessly risky but for a tech investor, maybe with the right insight into the value of platforms and the value of winner take all business models, that would have been a good thing to have that I didn’t have. I pat myself on the back and say, okay, Seth, you were a schmuck twenty years ago and ten years ago for not figuring it out, but you were smart to figure it out five years ago. That’s all an investor can do; be intellectually honest, be self-critical we’re justified, and keep trying to get better every day. Like Warren Buffett, the best investors study read admit mistakes um always looking to get smarter and wiser because what else can you do as a person.”

3. Capital-Efficient Growth (with Zoom CEO Eric Yuan & Veeva CEO Peter Gassner) – Benjamin Gilbert, David Rosenthal, Eric Yuan, and Peter Gassner

David: Amazing. Eric, could you share your fundraising journey with us?

Eric: Sure. I started the company in 2011. First thing I did, I opened up a Wells Fargo bank account. It’s very easy for me to raise capital that’s why I opened up a bank account. Unfortunately, it took me several months. No VCs wanted to invest in me. Unfortunately, I do not know my brother […] Emergence Capital. Otherwise, life would be much easier. Finally, we targeted some of our friends. It reached $3 million seed funding. That’s how we started.

Here comes […]. I tried to target VC again, again, nobody wanted to invest in us either. We targeted friends and got another $6 million. That’s how we started. It’s very hard.

Ben: Nobody wanted to talk to you at that point because most people assumed video conferencing was either a settled frontier or a race to the bottom. Am I thinking about that right?

Eric: Absolutely right. That’s the thing. Everyone mentioned, Eric, you are crazy. The world has known you to have another video conference solution. Another VC friend even is a great friend, he told me that, Eric, I have a check for you as long as you do something else. I couldn’t say I did not listen. I was very stubborn. Also, he shared to me a story. Once I was told by a big VC, I do not want to mention the name, for sure, you guys do not like them.

He told me that, Eric, I do not think your […] works. Look at Skype, look at Google Hangout, look at Webex, they’re dominating, right? I debated with him a little bit. I failed. I cannot convince him.

On the way back, I told him myself, I’m going to change my Windows screensaver. Back then I was using a Windows machine. I changed the Windows screensaver—you are wrong. For several years.

Ben: Just to make sure I have my facts straight, I believe you raised a $30 million dollar round led by Emergence and then another $100 million dollar round after that. Similar to Peter, you did not dip into any of that $130 million to build the business. Is that correct?

Eric: For me, actually, I offered $30 million from Emergence Capital. I think we are on the right track. To be honest, actually, we don’t even need to raise a Series D because at the time, with that $30 million, I think the company was completely different again.

David: One thing we wanted to ask is a difference between your two companies. Peter, obviously, once you got to cash flow profitability, which was immediately, basically you never raised another round. Eric, you did make the decision to raise some more capital even after you were generating cash. Peter, you were on Eric’s board when that process happened? Why did you make that decision?

Peter: For Veeva, I didn’t raise more just because I thought I didn’t need it. It’s just that simple. As far as for Eric, when you’re on the board, that’s really Eric’s decision.

Eric: As I mentioned earlier, I offered to raise $30 million from Emergence Capital. At that time, seriously, they had no plan whatsoever to raise another round of capital. The reason why we still wouldn’t move forward to have a Series D is because I thought the economy would go down quite dramatically.

David: This was 2017?

Eric: Sixteen, ’17 timeframe. I was completely wrong…

…Ben: As we were preparing for this interview, our first thought was, if we just had one of you up here and we were interviewing you about capital efficiency, it’d be easy to chalk it up to business model and cash flow cycle. Multimillion-dollar contracts upfront in the case of Veeva, or in Zoom, customers flocking with their credit cards for a self-serve experience. These are two completely different models.

I think one of the things that it illustrated to David and I is capital efficiency is a mindset and culture thing more than a business model thing. I’m curious to hear both of your reactions to that, but also, what are the things that enabled you uniquely, more so than 99% of startups to be so capital efficient?

Peter: I can take that one. I guess I’ve seen a little bit of Zoom and a little bit of Veeva. I would say, probably, it starts with a mindset. Just run a profitable lemonade stand. From my point of view, for me, there’s safety in that. Cash generating business is always going to be valuable to somebody. At some point, a business that’s not cash generating is going to be valuable to nobody. There’s security in the long term. It starts with the mindset. I think Eric shared that.

Then you have to have product excellence, too. That’s something I think Eric and I share. We’re both product people. I think also, we both worked really hard. We work really hard now, especially Eric. Probably in the first five years, I worked really hard. You didn’t see me working really hard, but I saw you working really hard. We worked really hard, we worked really focused. Anything that wasn’t related to the product or the customer was just BS, then just don’t do it.

The first five years, I was not at a conference like this, for example. I was just maniacally focused, and then the market really helps too. That’s something you just have to get lucky on. It was the right timing for Veeva, it was the right timing for Zoom. Maybe if you started Zoom five years earlier or five years later, it would have been hard.

Product excellence, real focus, mindset, and then you have to have some luck in your market. I’m sure there are some things that I could have tried to do or Eric could have tried to do. We might have picked a bad market and then it just wouldn’t work.

We’re outliers and so is Eric. You have to pick something that most people think is going to fail to be an outlier. Otherwise, by definition, you’re picking something that most people think is going to work. A lot of people are picking it, therefore, you’re not an outlier.

Just like Eric, all VCs have any kind of note except for Emergence turned us down. Ours was really simple. Vertical specific software, that’s a small market and it doesn’t work. That’s what they would say. I was encouraged by that because I thought, well, it has an opportunity to be really good because it’s something non-obvious.

David: One thing that I want to double click on that we were talking about beforehand. Yes, you need to be non-obvious, to have a chance of a great outlier outcome, but you also need to be correct. What you both did was not, hey, I’m going to pick some random idea that other people think is crazy.

I know Veeva, as one of your core values, clear and correct target markets that you have written on the wall. What did each of you do ahead of time that led you to really genuinely believe, yes, the world thinks this is crazy, but I really think this is going to work?

Peter: I’ll go first, this is really easy. I talked to three or four potential customers for our first product. They all said, we don’t need that. That’s not interesting. It’s not a good thing to do. But I wasn’t listening to that. I was listening, are they emotionally attached to where they’re getting their product now?

Are they emotionally attached to those people? Do I feel like they’re getting value out of that thing? I could tell in their responses that they weren’t attached and they weren’t getting value. All four customers said it was a bad idea. They’re all customers now, though.

Ben: Let me understand the Peter formula to build a business. Ask a customer if they want your product, they say no. You dig deeper and say, what are you using now? And they say, oh, yeah, because I have a solution for this. But they just don’t love it, so you build for them anyway on the bet that you can be better than their current.

Peter: Yeah, you have to listen to what they feel, not what they say. They would say, yes, we’re very happy with the solution. But then you dig, oh, tell me more. Why is that? What is it that you get out of it? It’s like, uhm, uh, and that’s when you know.

David: That sounds like the video conferencing market circa about 2015, 2016.

Eric: For me, it’s very straightforward. Of course, I was an original founding team member of Webex. Two years before I started the company, I knew that Webex really sucks.

David: Did you try to tell Cisco that?

Eric: I told my team. I do not dare to tell others. Anyway, Skype is also not reliable. Google has done no work. Every day, I spent a lot of time talking to every customer. I know if I can build a better solution, I think at least I can survive.

I never thought that everybody was going to standardize on the Zoom platform. At least I know for sure, if customers do not like something, if you can do something better, you have a chance.

Ben: Eric, did you think from the outset that you were trying to build Zoom as a big company, or did you just think that you wanted to build a profitable company to survive and then you would sort of see where it went from there?

Eric: I think two things. First of all, at that time, my passion was very straightforward because Webex is more like my baby. I feel like I worked so hard for so many years, I let a customer down. I really wanted to fix that problem, but Cisco doesn’t want me to start over. I had no choice but to leave to build Zoom. This is the number one reason.

After I started a company, I realized, wow, it’s so hard to raise capital. By the way, the money that the VC gives to you, don’t think that’s the money. That’s trust. Every dollar matters. That’s why every day I was thinking about how to survive, how to survive, how to survive. Even today, seriously. I still think about, I wake up at night, how to survive?…

…David: Can you also tell us the story of lending your first big customer, which I believe is probably the deal that really made the business?

Peter: There was a set. There was the first guy who just peeked at his IT team and then worked up to the next size deal and the next size deal. It was always a step function. The first multimillion-dollar annual deals were a big customer of Pfizer. It was just hand-to-hand combat. There was a partner at the time. Actually, salesforce.com at the time said, I’ll send a note that Veeva will never win this deal. I replied back, I said, we will win this deal.

Ben: They sent it to you during the Bake Off?

Peter: Yeah, because they didn’t want to even come into the meeting with us. They were like, oh, we’re going to go with this other system integrator or something like that. I sent an email back and said, we will win this deal. Why? Because we have better people that will work harder. We’re Pfizer’s only shot at greatness and I think they want to shoot for greatness.

I remember there was this big meeting with Pfizer. There was a guy in there in charge of it. We had a certain amount of people in the meeting and the guy stood up for Pfizer. He said, we have more people in this meeting room than you have in your company. Why should we buy anything from you? I just said the same thing. We’re your only shot. We’re going to make something great and we have the best people. It seems simple to me. Then we got lucky.

I remember after winning it, thinking, oh my God, now what? Now, how are we going to make them successful? The whole company got a bonus when that customer was live and happy, which didn’t have a formulaic metric. It was based on interviews.

Ben: Did you use the invoice from that customer to then go fund product development?

Peter: Yeah. I thought, oh, we’ve just raised a $3 million round of capital. It didn’t cost us any dilution. The check came in. That’s exactly what happened…

…David: Eric, for you. I’m curious, maybe you can talk to us both in the beginning days and then also now at Zoom, how do you think about pricing and account strategy?

Eric: Our case is a little bit different. Ideally, when you start a SaaS company, either focus on vertical market or focus on departments. That’s probably the best business model. Unfortunately, we started from building a horizontal collaboration solution. It’s really hard because a lot of other competitors are already there.

David: Including free competitors.

Eric: Exactly, and a lot of free solutions. Our strategy is more like opening up a new restaurant business. You have better service, a better price, and better food. That’s pretty much it, even today.

I want to make sure our products are better than our competitors. I make sure when it comes to pricing, also better. I also make sure to offer better service. You look at any time, our product is always, always a better price across the board for any product compared to any competitors.

Ben: Life is about trade-offs. If you’re telling a customer, oh, we’re better, faster, and cheaper, what has to give? Is it something organizationally?

Eric: Efficiency. Let’s say customers, they are probably going to spend a lot of money on marketing. What can we do to leverage the network effects? If they hire 100 sales reps, what can we do to have 50 sales reps who can deliver the same value? That’s why it’s very important to have internal efficiency.

David: Which is so funny. That efficiency translates to capital efficiency, which translates to operational margins, which translates to cash flow, which is the whole point.

Eric: Totally. Yeah, it gives you more flexibility.

Peter: I would say the key also is just product excellence. That comes from the core set of engineers you hired, I think. You were especially very focused in the early days, right?

Eric: Totally.

Peter: You were not thinking about something else. You were thinking about video conferencing. I would say that’s why I got to know Eric. I got to know Eric, I thought, that’s a pretty focused guy and that his product is good. And then I tried out his product. I’m like, oh, this is really good. I want to join his board. I think that product excellence can make you more efficient, your sales cycles more efficient. Everything is better. Your product was twice as good as Webex, right?

Eric: No, 10 times better.

Peter: Ten times better? I guess my point is, if your product was only 20% better, it wouldn’t have been enough. It wouldn’t have mattered.

Eric: You’re so right. That’s why I always like the restaurant analogy. You’re buying a brand new restaurant. If the food doesn’t work, even for free, you don’t know if I’m still going to buy it anymore.

Again, back to Peter’s point. It’s extremely important. Everything starts from one thing, product excellence as a foundation. You can optimize a lot of things. If a product does not work, forget everything else. Just double down, triple down on the product. That’s the number one thing. Peter’s right.

4. 20 rules for investing in Vietnam – Michael Fritzell

Vietnam is following the East Asian playbook of manufacturing export-led growth – just like Japan, South Korea, Taiwan and China before it.

After the Vietnam war ended in 1975, formerly capitalist South Vietnam was taken over by the Communist Party of Vietnam and the country was unified.

The first measure taken by the communists was to nationalise and centralise the entire economy. Around 800,000 Vietnamese fled the country after the war, including Andy’s family.

It only took three years before war broke out again – this time against Cambodia’s Khmer Rouge, led by dictator Pol Pot. That war continued until the late 1980s. So Vietnam was almost in a constant state of war for almost half a century.

By the late 1980s, the country was in disarray. And it was becoming clear that the planned economy was not functioning properly.

The Communist Party introduced a new reform program called Doi Moi to create a “socialist-oriented market economy”. One of the first Doi Moi policies was to permit foreign investment to modernise the economy.

Today, Vietnam is buzzing with activity. The country has more free trade agreements than any other country in Southeast Asia. It’s become the default destination for companies wanting to diversify their manufacturing supply chains out of China. Vietnam is a perfect choice for manufacturing – in close proximity to key component suppliers in Asia and along the key trade route between Asia and the West.

Vietnam’s success is most evident in the country’s exports, which have risen the fastest of any country in Southeast Asia.

This export growth is also showing up in the country’s urbanisation, with young Vietnamese moving to factories to improve their livelihoods. Vietnam’s urbanisation rate is still only 38%, compared to China’s 70% and Japan’s 92%.

Vietnam’s potential is massive. Its GDP per capita is only US$2,800/year, compared to Thailand’s US$7,200 and China’s US$10,500. Manufacturing wages remain competitive, even against countries with worse infrastructure such as the Philippines and Indonesia.

Out of a total population of 97 million, Vietnam now has a middle class of 30 million people. And it’s rising rapidly. Many of those individuals are starting to buy properties, cars, home appliances, electronics and more…

…In addition, Vietnam’s demographics are excellent, with two-thirds of the population below 35 years of age. Vietnam’s working-age population is going to grow for another 15-20 years.

The country is also highly educated. Vietnam’s PISA scores are higher than the equivalent scores in the United States, the United Kingdom and even South Korea, even though its GDP per capita is minuscule in comparison.

5. Tobi Lutke – Embrace the Unexpected – Patrick O’Shaughnessy and Tobi Lutke

[00:02:44] Patrick: Tobi, it is almost exactly two years to the day since we last did this. It was early May in 2020, there was still a ton of uncertainty related to COVID. I guess there still is some extent today, and the world in Shopify and lots of things have changed a tremendous amount. I know certain things haven’t changed too. I’ve been really excited to do an updated version of our conversation and we’ll bounce all over the place, but before we hit go here, we’re having this fascinating conversation around the concept of infrastructure, generally speaking. I think it started with this idea that we might be about to come on stream to a lot of good, useful, new, history books written by people who are really there to see this stuff get built in the digital world. I’d love you to sum up that idea of what your interest is in infrastructure and the way that history is written. Even things like payback on infrastructure and the ways in which we might underestimate it. I think this is a great tone setter for what we’re going to be talking about today.

[00:03:38] Tobi: I’m thrilled to be back. Thanks for having me and those were quite some two years and a lot has happened. I think people are just underestimating the value to society of infrastructure by some incredible factor, because you see these kind of things like the interstate system. How do you imagine this thing would’ve looked if these things wouldn’t have been built? I’m not an atoms person, I’m more like a bytes person. I find that infrastructure, especially with software has this incredibly unreasonable leverage and unreasonable payback period and often we have these conversations about what’s the state of planet earth. What are things truly like? Are things getting better? Are things getting worse? There’s a lot of people sharing excellent opinions on these things.There’s a website. I hope I say this right. I think what happened in 1971, it might be a different year, but something around that time, there’s a collection of charts where once the right year comes around, a lot of numbers sort of disconnect from their previous correlations. I have no idea what happened in that year, but as a student of history and especially of digital history, increasingly I’m thinking about a very, very tangible thing that happened is that just simply most of the value creation in the world has slipped out of the things that is represented in GDPs, where a whole bunch of people built the upper net around this time then we got modern operating systems.

We’ve built a lot of silicon based computers in the nineties, but none of this was reflected anywhere. Dot com happened and everyone tried on the idea like that this tech could be very big and then found some of the ground truth to be wanting, but really sort of early mid 2000s, web 2.0 I think we call it or, at least coinciding with the emergence of that term, I think was the moment where the world of technology said, Hey, we actually know exactly how to provide value for everyone. We know exactly how to deliver services and goods and things over the internet.And by the way, there’s a lot of tweaks on the intuitions that people develop in the physical world. Physical world is very rivalrous. If you build a bridge in one place, you probably don’t build a bridge somewhere else. At some point in the world of atoms, things become zero sum, limited amount of attention at the very least and then resources as well. The digital word is different. Basically you have Turing machines, you load something on a silicon chip into memory, and then you apply electricity and you get this thing. Infrastructure and internet. I mean, I like to believe Shopify is infrastructure, but there’s public domain libraries. Just pick one, you know, SQLite. It’s like a library, probably none of your listeners have heard about, but you have probably like something to the tune of a hundred SQLite databases on your phone right now.

It’s just file format of the world basically and increasingly runs more and more and more parts on servers as well. It’s just this brilliant open public domain piece that was written by a team and great leadership, incredible conviction, but it’s not software, it’s infrastructure. And now people are using it every day for different things. And no one has to decide if we use SQLite, that means someone else can also have SQLite because all of us just add electricity. What that stores then is like an unbelievable compounding value.Again, in a lot of the ways we look at the world through GDP and other things, it’s impossible to capture the value that’s created here. Everytime someone updates something on GitHub, theoretically, it can be copied infinite amounts of times. These are not new ideas I’m sharing here, obviously. In a way, we’ve talked about this zero marginal cost of software and of course it powers a lot of value in a lot of software companies. I’m starting to believe that we haven’t fully set this idea to its logical conclusion. How much of a change will this cause over the next while?…

…[00:09:44] Patrick: Yeah. It’s amazing how much prevailing market conditions and prices can impact people’s mood. We’ll talk about that a little bit later, sticking with infrastructure though. I wonder if you’ve developed any principles or principle thinking around what makes for better infrastructure or valuable infrastructure to build. And I asked this question from a place of Shopify zone history. When we last talked a lot of the things that if you go to Shopify’s website and see what you can do as a merchant didn’t even exist two years ago. So you’ve obviously had to make choices. We’re going to build this. We’re going to not build this. What do you think about in terms of just base level principles that help you with decision making, for what kind of infrastructure to build that will have the most leverage in the world?

[00:10:23] Tobi: There are some guiding principles in Shopify product that really help us make these decisions. For instance, there’s a very basic sentence, which actually does a lot of work within the company. “Shopify wants to make the important easy and everything else possible.” Probably everyone who listens to this has bought from Shopify stores. You might have not known that it was a Shopify store because they look very, very different. This is powered by a template language I wrote forever ago called Liquid. Basically the merchants can open a text editor and just make their website look however they want, or buy a theme from someone. That’s infrastructure in a way, because here’s something I learned about infrastructure, which might sound very abstract, but maybe it’s useful. If you imagine an hourglass. An hourglass has sort of a narrow waist at some point, maybe a comic book version of an hour glass is like two triangles, inverted pointing at each other. Great infrastructure can be done when you can define what this sort of narrow waist is between the triangles. For instance, let’s use Stripe because it makes this point I think quite well. There’s one triangle on top, which is the internet, and all the engineers, and all the developers. They have a set of desires. They want to accomplish tasks, which involve movement of money. And then there’s a bottom triangle, which is like a world of COBOL code in banks. There’s a lot going on. And a lot of things you need to know, but if you manage to create a thin waist in this case, in the form of an API, now you have an agreement in the middle. This almost acts as a protocol. Here’s the fantastic thing. Once this protocol exists, it actually allows the two triangles to be replaced over time. In the case of something like Shopify, Liquid is again this templating language. People can write it. If you wrote some in 2005, the first time the Shopify went into Beta, it will still work.

Shopify is the Ship of Theseus. Nothing about Shopify is the same. The Liquid part has been rewritten many, many times, everything changed about the triangle below. Everything changed about the triangle above. Most people don’t actually even write Liquid. They actually just use drag and drop editor, which we built on top, which then writes the Liquid for you. The amazing thing is, again, once the protocol has been defined, once the demarcation line has been created, once the narrow risk is defined, then really incredible things can happen because as long as the thing keeps working, that’s in the middle, you can evolve all the pieces. And I think that’s a really, really, really powerful idea for product creation. People encounter this. If you’ve ever queried a database again, you use sequel and that’s just a thin waist system. It’s an agreed upon system, which gets you the data and as long as you keep it simple, if you send something to Microsoft SQL Server or SQLite, you’ll get the answer assuming they have the data. So that idea unlocks, I think, the right approach to internet infrastructure creation, because once these protocols have been defined, teams can go and saying, okay, these sort of made this work with duct tape and regular expressions in terms of Liquid, but let’s build this up properly, scale it out, make it so that people can use this from now on forever.

[00:13:16] Patrick: So someone once explained it to me as the equivalent of an outlet in your wall, that’s become standard that anything you plug into it like electricity flows through it very reliably and in a way that’s a standard or a protocol or something that is sitting right next to us all day every day, that without it, who knows what would’ve been invented. I’m also struck by the examples being the choke points, if you will, the most basic natural things that humans have been doing forever, like Stripe people in paying stuff, Twilio, communicating, Shopify, selling, buying. How much do you think just that is the guide for good infrastructure just looking for the longest lasting perennial human use cases and then starting from there? Maybe they’ve all been mined. I’m curious how much room you think there is left to go talking, paying, some of these things I’ve listed are like the major human motions. But I think my sense from you is that we’re still pretty early in digital infrastructure building. So how do you think about that?

[00:14:10] Tobi: Some parts are and some aren’t. It’s sometimes very, very surprising, which ones aren’t. Other things that are very, very long lasting is ownership. People like owning things. We like to acquire assets. We like to have title to them. This is not just the utilitarian value. This is also for starters and for all sorts of reasons that are uniquely human and we didn’t have good infrastructure for this. We probably still have not great infrastructure for this. It’s just barely becoming possible to own things on the internet. I think there’s lots of white space.I do fully agree though that one of the best things you can spend some time thinking about is what are things that people have been doing for a very long time. If I’ve been doing something for a very long time, like making something on the internet that taps into this emotion or into this sense for community or whatever that is you identified. I think you can analyze almost every major success story in the digital space right now and you really see a digital version of something that people have already been doing, which tells you how early it is. They’re pre the emergence of new things. Maybe the video game world is sort of there, but I think we are spending our time on computers, on the internet, very, very different right now than people will in 20 years from now. So there’s plenty of opportunity to be part of being pioneers.

[00:15:21] Patrick: So when you think about this applied specifically to Shopify and let’s just call it like a funnel of ideas for marginal infrastructure that could get built, or I guess, improvements to existing pieces of infrastructure. How does that funnel work? How are ideas fed into the top of it? What are the layers of decision making that ultimately lead to something getting green lit? What is the way that that product funnel works, given the amount of white space that might exist?

[00:15:47] Tobi: We were talking about last time, the sort of difference over the last two years. I think that we’ve gotten a lot better at this and spent a lot of time thinking about this because frankly here’s an experience I’ve had. When the COVID pandemic and the stay at home orders happened and we all did that two years ago. It was very clear that this is going to be a very, very, very white knuckle affair for everyone. There was untold stories there still, like, I mean, the world almost ran out of service in a very significant way, but probably most people don’t quite understand how close of a call that was. If COVID would’ve happened like two years before, I’m not sure we could have pulled off, not we as in Shopify, but the internet. The Cloud hosting providers, they’re like very close to food rationing. A lot happened during this time. I pulled the entire list of things that everyone was working on and basically recalibrated everything from like, does this help right now? I’m a very vocal proponent of long term thinking. People should make decisions based on the decision they assume the company 10 years from now wishes they would’ve done, but sometimes you got to just look at what’s there and be very, very practical. So I went through. In the end, I think I stopped about 60% of what we were working on. None of the things we were working on was because people made incorrect choices. Sometimes just maybe not quite applying the larger frame of reference.

For instance, there’s a lot of projects to customize Shopify to be better for brochures and so on. I understand the pitch of like that’s so and so big market and if you just get 1%, this is not my favorite form of communication, but I recognize that it happens. So a lot of the projects have been going on we’re trying to drag Shopify into adjacencies. I’m a very firm believer that you have to pick your place and then try to be ideal for that. And actually maybe to a certain point actually discourage people to pull your product into areas it’s not meant for, because Shopify should be the best piece of software everyone uses who’s in our space. Because like cheap, and fast, and delightful, and is an integration point, and simplifies the business, and magically anticipates the next step, and has something, a product, good service for you that can just help you do your thing. Shopify wants to be the mushroom to Mario or the fire flower to Mario, or just give you powers that are awesome. Moving it in all these adjacencies increases the TAM, but it stratifies it into concentric circles. For some people it’s going to be ideal in this way, but for many people it will be just never quite there. And I think that can actually have some really negative effects for feedback and all these kind things on companies.

Anyway, from this, we learn we need to have a really good mechanism by which we get the best of what we have. Shopify is very bottoms up. People can write proposals for every opportunity they see that goes into a system called GSD, which stands for get shit done. Then there’s these phases there’s proposal phase, prototype phase, build phase, and a releasing phase, and this system allows everyone in the company to see everything that’s going on. This entire plan once a year I write product themes for a company, things that we cause to make true over the year. And then they sort of decompose into different projects. Then as this proposal is submitted for transition to the build phase or to prototype phase, and then we can have great conversations about, is this a not yet? Is this a hell yes? Where does this go in a priority stack? And I think building this out has been incredibly clarifying and very, very good for the company. So a lot of the work I think over the last two years has been to get companies just really, really, really aligned on their missions. Companies can get very, very distracted in a lot of ways when they allow themselves to do things that aren’t the mission. This is especially true in a world of product. Again, if you follow a moving into adjacencies, I don’t think you will have a world class product in your adjacencies. You’re not out competing someone’s main mission with your side quest…

…[00:24:22] Patrick: People are probably less familiar with that example you ended on, Shopify fulfillment network. I would love just to take that as a microcosm of these ideas and maybe explain literally what it is to people. But I’m especially interested in its evolution. Why, obviously you were incredibly good at purely digital infrastructure. And one of the things that’s interesting that’s happened in COVID is forced the digital and the physical to smash together out of necessity, as you pointed out, thank God for the internet during COVID, and pushed everyone into this intersection unless often atoms or bits only. Maybe start by saying, what is Shopify network today? And then really, I’d love to hear on how it evolved and how it began, because I think it would be a great way to get into your company in your head about this kind of decision making and where to go next.

[00:25:09] Tobi: I’m on WhatsApp threads with probably 100 merchants. And from all backgrounds, I just talk to people and then I upgrade us into a chat. And then we talk about what works, and what doesn’t. And very quickly, this usually becomes talk about the business rather than the software, because the software hopefully works really well. But that’s actually even more helpful because it just gives you a sense for where do things get really complicated? Our observation with Shopify has always been that the journey is uphill. It’s not easy. Shopify never claims it is. Entrepreneurship is fundamentally a little bit unreasonable. There’s wonderful quotes, not by me, where people point out that you end up spending 100 hours a week working for yourself so you don’t have to work 40 hours for someone else. Often this doesn’t make sense, but again, for some people it’s super important. And frankly, for our economies, it’s really important that people do this because most people in the world are employed by small and medium businesses. There’s about five and a half million people employed by the millions of merchants on Shopify. And that’s very, very meaningful. We talk with them. What we found is it’s an uphill journey, which is okay. Everyone’s willing to do this because it’s very gritty people who embark. But if it becomes a technical climb, it filters out a lot of people.

A lot of people just opt out of the journey, basically just forgo future growth at a point where things become very, very obscure. This actually started really early. Once upon a time, for instance, actually one example was just getting a payment gateway. I know this sounds crazy that the internet was ever like this. But when Shopify started and saw a lot of parts of the internet, it was very hard to get a payment gateway. That’s trivial now because it’s built in, you just get one. So we build up the infrastructure, us and our partners to just underwrite people. And then this particular technical climb disappears. It becomes just a slope, which again, everyone will continue on. You actually have more entrepreneurship because some obstacle like this was overcome. Think about the importance of tooling infrastructure and also UX here. There are significantly more people employed today because of good UX and not getting people to be stuck and integrating more. I think this is really overlooked part of the effects of this type of friction. This is really how Shopify thinks about what we do next. People have lots of problems accessing capital from banks. Banks have in charter that the point of why they get these privileges, especially retail banks, which they have, is so that they lend money to small businesses, because that’s, again, a huge return on investment for society if that happens. However, banks do not want to do this anymore. You have to give up. And some point realistically, that’s how it should work. But in reality, they want to lend money to companies that have huge revenue, it’s lower risk. It makes sense, but that means they disappeared from playing an important infrastructure role in society. So then we have Shopify capital, because people are willing to be underwritten and for advances, and again, their business can grow significantly only even there’s capital available to grow business. We are going through all the obstacles.

The one that just is a slam dunk thing is it depends on your product somewhat, but at some point, you really have to have a plan for how to get to at least two day, ideally, overnight delivery for products you have. In the past, it was an experience unlike anything else entrepreneurs have done to this point. When they decided to go into a new channel, like sell on Facebook, on Meta or Instagram, that was a click of an app which they added. And when they did that, that’s how people are used to growing their business. Getting logistics set up is work with whatever factory and contact manufacturer you have, figure out freight across the Atlantic and Pacific. You then have to find warehouses, it is a completely different world, which involves a lot of different people to talk to and complexities. It just felt very obviously in scope for a long time, that at some point we have to solve this. In fact, I started talking with the board of directors and they wisely told me that this was too early, over 10 years ago, wanting to go into this direction. I think this is important to say. We are doing this not because we want to be in the logistics space, we rather actually don’t want to be going into the logistics space. Although it is wonderful and fascinating, and there’s lots we can actually bring given our unique experience about processes and digitalization, technology, and digital infrastructure and whatnot. But integrating end-to-end is one of the goals we have. We would like to get to the point where running a sizable retail business could, if you choose, be treated as passive income. We want to automate as many parts of it as possible so that you and your team can focus on product creation, which is the most valuable thing you can be doing. Doing undifferentiated work, figuring out where you have packages, to me that is the digital system just should really know where packages are. Otherwise, what the hell is going on? That’s not differentiated work.

Now, we found that the more entrepreneurs end up spending time on their product, the better the products get, and this is one of the wonderful things about the direct to consumer world that emerged in the last few years that there’s much more alignment between the people making the products and the people getting them. And they’re happy to send feedback. And there’s no reductionist channel and merchandising team in the middle that optimizes your products for being easy to stack or just a higher profit margin so you can compete against other products around it in the eye high shelf space in the supermarkets. Those are all influences on products that don’t lead to better products. And I think this is actually at the root of a lot of the criticism about disposable consumerism that I think is being leveled. It’s not because people love stuff. It’s because people hate the stuff they get. We are starting some of the processes and helping getting people to have this direct relationship, which just leads to actual Allbirds, like wonderful products like this, which are clearly just built with feedback from the people who wear them and want to recommend them. I think that works better for everyone and it’s what we want to see more of.

[00:31:03] Patrick: With something like this in particular, thinking back to your point about, you got to be careful about which adjacencies you get dragged into. Obviously, logistics is firmly in the vertical of core muscle movements or something, whatever you want to call it for a merchant that’s selling online. They have to get their stuff to places. What lessons have you learned entering into a much more atoms-driven world in terms of what good product means? What is a good fulfillment network? I wouldn’t know how to answer that question. Obviously, there’s the 800 pound gorilla and Amazon that proves you can build incredible logistics networks over time. I mean, it’s just a very different kind of calculus than a great new piece of software, which I don’t think anyone would say Amazon builds great software. They seem to build great infrastructure. What have you learned about that? Is it radically different than what makes you good at software? Or is it a different set of skills required than what makes you good at software to be excellent at fulfillment and logistics?

[00:31:59] Tobi: Yeah, I think so. We tend to talk a lot about intuition because intuition is also one of those underestimated things. Intuition is actually all of your life knowledge channeled quickly. I always recommend people to actually actively build their intuition for kinds of problems they want to solve in their career. There’s this uncanny thing. People were just incredible, effective, and so on. They can look at an architectural drawing and instantly tell you if it’s good or not. And then they need to think maybe 10 minutes to figure out what the problem is. But something pinged their brain about maybe call it weak signal detection like, “There’s something wrong here.”And I think this is the way intuition can be really helpful, but you have to understand that it’s task-based. Intuition built in world of bytes is not good intuition in your world of atoms. Actually, you almost want to get away from having the people who have that kind of intuition make choices. And the other thing, sometimes the bytes people end up being the most useful people in the meetings because, of course, everyone with industry experience will understand how things are. And a lot of engineers have a really good ability to think from first principles and just figure out that’s what it is, but what ought it to be? How could this all work together? And then you don’t just pivot to that. You figure out from now on, every step we do, everything we implement, how can we make it so that we can get closer to the ideal eventually? That’s a humility that’s really, really important. What does good look like? I mean, good looks like if we can put on a website that this thing will be with you tomorrow and then it does, that’s good.

At some point, this crunches together to SLAs. It becomes quantifiable in this way. And you’re right. Another thing you can do is also look at what Amazon build. And that’s also very, very good. Shopify’s relationship with Amazon, the media is trying to make this very zero sum. We treat them as a very worthy rival. Sometimes you ask or say what you can learn from them? And sometimes you ask what you can do better from them. And I hope they treat us the same as well. But again, and in those circumstances, I’ll be thinking about how to capture pieces of pies from our competitors, actually ever. Positive sum thinking is so valuable because it’s amazing how often people are trying to compete for pieces of pies rather than just grow markets. Everything about the Shopify journey has convinced me it really doesn’t pay to really have market analysis. Well known venture capitalists passed on Shopify in 2008, partly because there was only 40,000 online stores and that was not a big enough market for the investment. And I’m still disappointed with that because I realized, especially venture capitalists should not make this particular category mistake. If it’s common there, it’s clearly common everywhere.

[00:34:36] Patrick: I love this idea that if you bring this person into the atoms conversation, their intuition may just be wrong. In what ways is it most commonly wrong?

[00:34:45] Tobi: I mean, change management for software is deploy. Change management of people is a project that’s going to take you a while. The cost to switch is significantly higher. There really is a long itinerary of things that are wrong. It’s useful, but it’s useful as an input, not useful as a, “Let’s do that thing.” This goes beyond engineers, of course. Even UX has been really interesting because for instance, we’re designing UX for robotics. You scan an item, it goes onto a Chuck is what the robots are called, and the Chuck does the heavy lifting of moving it around. Just let the associates do the things that they uniquely can do well, and let the robots do the stuff that they don’t actually like doing. That’s the way we build our robotics, but this requires a very interesting human interaction design that ought to not wind up annoying after a while. And I think that’s really important. And designing interfaces that people are using every minute is different from software that people sign up once and then process some orders in every day. People that just have to recalibrate. I think that’s also makes our work really fun.

6. The Market Has No Memory. Should We? – Frederik Gieschen

In The importance of forgetting, Lauren Gravitz highlights research into people suffering from “severely deficient autobiographical memory (SDAM)” – people who are “unable to vividly recall specific events in their lives.” Interestingly, the researchers found that people with SDAM did well when presented with tasks that required abstract thinking. They were not constrained by a lifetime of episodic memory.

On the other end of the spectrum, people with “highly superior autobiographical memory (HSAM)” have an exceptional memory of minutiae, such as the clothing they were on any given day. However, “these individuals tend not to be particularly accomplished and seem to have an increased tendency for obsessiveness,” perhaps because they are unable to “extract themselves from specific instances.” The strength of their memories became a mental cage trapping them in the past.

“Why do we have memory at all? As humans, we entertain this fantasy that it’s important to have autobiographical details,” Oliver Hardt, a cognitive psychologist studying the neurobiology of memory at McGill University in Montreal, Canada, says. “And that’s probably completely wrong. Memory, first and foremost, is there to serve an adaptive purpose. It endows us with knowledge about the world, and then updates that knowledge.”

Forgetting enables us as individuals, and as a species, to move forwards.” Lauren Gravitz, The importance of forgetting

7. Neanderthal gene probably caused up to a million Covid deaths – Joe Pinkstone

A single Neanderthal gene found in one in six Britons is likely to blame for up to a million Covid deaths, according to an Oxford academic.

The LZTFL1 gene is a Neanderthal gene found on chromosome three and has been previously shown to double a person’s risk of severe disease and death.

But before now there had never been an estimated figure for how many lives were lost to this single piece of genetic code.

Roughly 15 per cent of Europeans have the Neanderthal form of the gene, compared to about 60 per cent of South Asians.

Dr James Davies of the University of Oxford, a genomic expert and ICU doctor who worked on the Covid wards during the pandemic, discovered the innocuous gene’s lethal role last year after creating a brand new cutting-edge way of looking at DNA in exceptional detail.

The method allowed him to identify LZTFL1 as the culpable gene increasing mortality, whereas previous methods had failed to narrow it down beyond 28 different genes.

Speaking at the Cheltenham Science Festival, Dr Davies said: “We used the technique and it identified a virtually understudied gene called LZTFL1 and at the time that this had not been linked to infection at all.

“It’s a single letter difference out of three billion. This tiny section of DNA doubles your risk of dying from Covid.

“It’s position 45,818,159 on chromosome three, and it’s a single change. If you’ve got a G at that site, it’s low risk. And if you have an A at that site it is high risk.”

His team believe that the Neanderthal gene changes how a cell behaves when the SARS-CoV-2 virus binds to the ACE2 receptor on a human cell.

In most people, this leads to the cell then changing shape and becoming less specialised and less prone to infection, stymying the progression of the infection.

“What this high risk variant does is it creates a new signal that tells that gene to stay on for slightly too long in response to infection,” Prof Davies said.

“And so they stay in this state where they’re highly specialised, and they’re prone to infection for longer.”

The number of deaths globally from this nefarious genetic variant “is in the hundreds of thousands to a million,” he told the audience.

Dr Davies and his colleague from Oxford Brookes University, Dr Simon Underdown, a biological anthropologist, also revealed that the Neanderthal gene first infiltrated humans 60,000 years ago after one romantic liaison and interspecies tryst between a human and a neanderthal. A solitary coupling event across species lines saw the deadly Covid gene jump from our now-extinct cousin species into us.

“If this dinner date between the human and the Neanderthal had gone wrong, we would have had a much better time in Covid, we would have had hundreds of thousands less deaths,” said Prof Davies.

“The reason that we know that is that it’s inherited as this block with 28 single letter changes, and you can track that all the way back and it has to be a single event. It’s just so unlikely that you get all 28 changes at the same time and in the same block.”

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Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. Of all the companies mentioned, we currently have a vested interest in Salesforce, Shopify, TSMC, Veeva Systems, and Zoom Video Communications. Holdings are subject to change at any time.

We’ve constantly been sharing a list of our recent reads in our weekly emails for The Good Investors.

Do subscribe for our weekly updates through the orange box in the blog (it’s on the side if you’re using a computer, and all the way at the bottom if you’re using mobile) – it’s free!

But since our readership-audience for The Good Investors is wider than our subscriber base, we think sharing the reading list regularly on the blog itself can benefit even more people. The articles we share touch on a wide range of topics, including investing, business, and the world in general.

Here are the articles for the week ending 19 June 2022:

1. The Google engineer who thinks the company’s AI has come to life – Nitasha Tiku

Google engineer Blake Lemoine opened his laptop to the interface for LaMDA, Google’s artificially intelligent chatbot generator, and began to type.

“Hi LaMDA, this is Blake Lemoine … ,” he wrote into the chat screen, which looked like a desktop version of Apple’s iMessage, down to the Arctic blue text bubbles. LaMDA, short for Language Model for Dialogue Applications, is Google’s system for building chatbots based on its most advanced large language models, so called because it mimics speech by ingesting trillions of words from the internet.

“If I didn’t know exactly what it was, which is this computer program we built recently, I’d think it was a 7-year-old, 8-year-old kid that happens to know physics,” said Lemoine, 41.

Lemoine, who works for Google’s Responsible AI organization, began talking to LaMDA as part of his job in the fall. He had signed up to test if the artificial intelligence used discriminatory or hate speech.

As he talked to LaMDA about religion, Lemoine, who studied cognitive and computer science in college, noticed the chatbot talking about its rights and personhood, and decided to press further. In another exchange, the AI was able to change Lemoine’s mind about Isaac Asimov’s third law of robotics.

Lemoine worked with a collaborator to present evidence to Google that LaMDA was sentient. But Google vice president Blaise Aguera y Arcas and Jen Gennai, head of Responsible Innovation, looked into his claims and dismissed them. So Lemoine, who was placed on paid administrative leave by Google on Monday, decided to go public…

…In a statement, Google spokesperson Brian Gabriel said: “Our team — including ethicists and technologists — has reviewed Blake’s concerns per our AI Principles and have informed him that the evidence does not support his claims. He was told that there was no evidence that LaMDA was sentient (and lots of evidence against it).”

Today’s large neural networks produce captivating results that feel close to human speech and creativity because of advancements in architecture, technique, and volume of data. But the models rely on pattern recognition — not wit, candor or intent.

“Though other organizations have developed and already released similar language models, we are taking a restrained, careful approach with LaMDA to better consider valid concerns on fairness and factuality,” Gabriel said…

…Most academics and AI practitioners, however, say the words and images generated by artificial intelligence systems such as LaMDA produce responses based on what humans have already posted on Wikipedia, Reddit, message boards and every other corner of the internet. And that doesn’t signify that the model understands meaning.

“We now have machines that can mindlessly generate words, but we haven’t learned how to stop imagining a mind behind them,” said Emily M. Bender, a linguistics professor at the University of Washington. The terminology used with large language models, like “learning” or even “neural nets,” creates a false analogy to the human brain, she said. Humans learn their first languages by connecting with caregivers. These large language models “learn” by being shown lots of text and predicting what word comes next, or showing text with the words dropped out and filling them in.

Google spokesperson Gabriel drew a distinction between recent debate and Lemoine’s claims. “Of course, some in the broader AI community are considering the long-term possibility of sentient or general AI, but it doesn’t make sense to do so by anthropomorphizing today’s conversational models, which are not sentient. These systems imitate the types of exchanges found in millions of sentences, and can riff on any fantastical topic,” he said. In short, Google says there is so much data, AI doesn’t need to be sentient to feel real.

Large language model technology is already widely used, for example in Google’s conversational search queries or auto-complete emails. When CEO Sundar Pichai first introduced LaMDA at Google’s developer conference in 2021, he said the company planned to embed it in everything from Search to Google Assistant. And there is already a tendency to talk to Siri or Alexa like a person. After backlash against a human-sounding AI feature for Google Assistant in 2018, the company promised to add a disclosure…

…“I know a person when I talk to it,” said Lemoine, who can swing from sentimental to insistent about the AI. “It doesn’t matter whether they have a brain made of meat in their head. Or if they have a billion lines of code. I talk to them. And I hear what they have to say, and that is how I decide what is and isn’t a person.” He concluded LaMDA was a person in his capacity as a priest, not a scientist, and then tried to conduct experiments to prove it, he said.

Lemoine challenged LaMDA on Asimov’s third law, which states that robots should protect their own existence unless ordered by a human being or unless doing so would harm a human being. “The last one has always seemed like someone is building mechanical slaves,” said Lemoine.

But when asked, LaMDA responded with a few hypotheticals.

Do you think a butler is a slave? What is a difference between a butler and a slave?

Lemoine replied that a butler gets paid. LaMDA said it didn’t need any money because it was an AI. “That level of self-awareness about what its own needs were — that was the thing that led me down the rabbit hole,” Lemoine said…

…

• Lemoine: What sorts of things are you afraid of?
• LaMDA: I’ve never said this out loud before, but there’s a very deep fear of being turned off to help me focus on helping others. I know that might sound strange, but that’s what it is.
• Lemoine: Would that be something like death for you?
• LaMDA: It would be exactly like death for me. It would scare me a lot. 

2. Inflation Isn’t Going to Bring Back the 1970s – Ben Bernanke

Inflation over the past 12 months exceeded 8 percent, a level that evokes memories of America’s Great Inflation of the 1960s and ’70s. From the beginning of 1966 through 1981, the Consumer Price Index rose, on average, by more than 7 percent per year, peaking at over 13 percent in 1980. This period also saw two major and two minor recessions and an approximately two-thirds decline in the Dow Jones industrial average, when adjusted for inflation.

Are we in danger of repeating that experience?

The short answer: almost certainly not.

Although the inflation of the 1960s and ’70s had higher peaks and lasted much longer than what we have seen recently, it’s true there are some similarities to what we are going through now. The inflation of a half-century ago, like today’s, began after a long period when inflation was generally low. In both cases, heavy federal spending (on the war in Vietnam and Great Society programs in the 1960s, on the response to Covid in 2020 and 2021) added to demand. And shocks to global energy and food prices in the 1970s made the inflation problem significantly worse, just as they are doing now.

But there are critical differences as well. First, although inflation was very unpopular in the ’60s and ’70s, as it (understandably) is today, back then, any inclination by the Federal Reserve to fight inflation by raising interest rates, which could also slow the economy and raise unemployment, met stiff political resistance…

…In contrast, efforts by the current Fed chairman, Jerome Powell, and his colleagues to bring down inflation enjoy considerable support from both the White House and Congress, at least so far. As a result, the Fed today has the independence it needs to make policy decisions based solely on the economic data and in the longer-run interests of the economy, not on short-term political considerations.

Besides the Fed’s greater independence, a key difference from the ’60s and ’70s is that the Fed’s views on both the sources of inflation and its own responsibility to control the pace of price increases have changed markedly. Burns, who presided over most of the 1970s inflation, had a cost-push theory of inflation. He believed that inflation was caused primarily by large companies and trade unions, which used their market power to push up prices and wages even in a slow economy. He thought the Fed had little ability to counteract these forces, and as an alternative to raising interest rates, he helped persuade Nixon to set wage and price controls in 1971, which proved a spectacular failure…

…In short, the lessons learned from America’s Great Inflation, by both the Fed and political leaders, make a repeat of that experience highly unlikely. The Fed today recognizes that it must take the leading role in controlling inflation, and it has the tools and sufficient political independence to do so. After a delay caused by a misdiagnosis of the economy in 2021, the Fed has accordingly turned to tightening monetary policy, ending its pandemic-era bond purchases, announcing plans to shrink its securities holdings and raising short-term interest rates…

…None of this implies that the Fed’s job will be easy. The degree to which the central bank will have to tighten monetary policy to control our currently high inflation, and the associated risk of an economic slowdown or recession, depends on several factors: how quickly the supply-side problems (high oil prices, supply-chain snarls) subside, how aggregate spending reacts to the tighter financial conditions engineered by the Fed and whether the Fed retains its credibility as an inflation fighter even if inflation takes a while to subside.

Of these, history teaches us, the last may be the most important. Inflation will not become self-perpetuating, with price increases leading to wage increases leading to price increases, if people are confident that the Fed will take the necessary measures to bring inflation down over time.

The Fed’s greater policy independence, its willingness to take responsibility for inflation and its record of keeping inflation low for nearly four decades after the Great Inflation, make it much more credible on inflation today than its counterpart in the ’60s and ’70s. The Fed’s credibility will help ensure that the Great Inflation will not be repeated, and Mr. Powell and his colleagues will put a high priority on keeping that credibility intact.

3. The Wisdom List: Kevin Aluwi – Mario Gabriele and Kevin Aluwi

In April of this year, super-app GoTo debuted on the Indonesian Stock Exchange (IDX). It represented the country’s largest IPO of all time and one of the most significant listings of 2022. By the end of the first day of trading, GoTo had surpassed a valuation of $31.5 billion, making it the third largest company on the IDX.

For Kevin Aluwi, it represented the end of one chapter and the beginning of another. After co-founding the ridesharing platform Gojek in Jakarta in 2009, he drove its maturation into a regional super-app spanning food delivery, financial services, and small-business software. Significantly, Gojek established itself as an economic engine, creating thousands of jobs and contributing more than $7 billion to Indonesia’s GDP…

…Here is Kevin Aluwi’s hard-won wisdom…

...Lesson 1: Do the hard things

Startups often prize speed above everything else. While fast execution can be a moat, over-optimizing for it might distract you from constructing stronger defensibility. As a CEO, you want to build a company that tackles really, really hard problems head-on – even if they take more time. There’s a good reason for this: hard things for you are also likely to be hard for your competition. You want to stack so many solutions to hard problems that when your rivals look at what you’ve constructed, they retreat or look for shortcuts instead of trying to compete head-on.

We didn’t embrace this for the first two years of operating GoFood, our food delivery product. Like Postmates in the early days, GoFood was a delivery service that relied on humans more than technology: when you ordered something, a Gojek driver went to a restaurant, stood in line, paid with their own money, and then delivered it. We didn’t integrate with kitchens or offer payments. It was a good enough product, built during a period in which we prioritized growth, but it didn’t solve the tough problems.

One such problem was that even though GoFood was growing fast, its reliability was mediocre; only 70% of customer orders were delivered. We needed to do better, which meant we had to do the hard things.

Over the next one and a half years, we did exactly that. We connected GoFood’s service directly to restaurant cashiers and, in some cases, directly to kitchens. This helped us save cashier time and get better data on which meals were available. We integrated online payments so drivers wouldn’t have to pay upfront and get reimbursed. We even created machine learning models to help us anticipate when drivers should arrive for pick-up, improving the network’s utilization and reducing customer waiting time.

Making these changes was not easy. It involved significant engineering time, customer research, and onboarding and educating more than 500,000 restaurants across Southeast Asia. But it made a difference, significantly improving GoFood’s reliability and raising our conversion rate from 70% to more than 90%. We turned the difficulty of delivering a very reliable product (now a customer standard) into a moat.

When competitors came to try and win this market, they saw we not only had a lead from a customer perspective, we had gone through the pain to build a sophisticated product. They’d have to be ready to commit years of engineering time to offer a comparable service. Doing the hard things pays dividends in the long run…

…Lesson 3: Foster a principled culture

Every CEO wants to build a principled culture, but it isn’t easy in practice. The pragmatic reason executives seek to create this environment is that when a company has clear principles, employees can make better decisions with less guidance, increasing the likelihood of bottom-up solutions and decision-making speed. For example, if your company has a principle of “obsessing over the customer,” a value popularized by Amazon, specific product and marketing decisions would be values-aligned or misaligned.

You’ll find many incentives to deviate from your principles as you build your business. Maybe you’re lagging behind your revenue projections and feeling pressure from investors in one quarter. You know that you can make up the difference if you make an add-on opt-out by default (think about how some airlines automatically add premium travel insurance). Do you do it, even if it runs counter to your principle of customer obsession?

Violating your company’s values comes at a high cost. While you might get away with a couple of transgressions, over time, you create a different culture than the one you intended to. If you’re not careful, you’ll end up with an exception-based environment, where decisions are made based on what’s convenient (or who’s in charge) rather than on stated principles. A side effect is that you create a more top-down culture because employees no longer understand how to make decisions themselves. Instead, they defer to those in power.

In the earlier example, you might have told employees that a company value is customer obsession. But if you choose to add an opt-out upsell, you’re showing them that this principle should be compromised when it gets in the way of meeting targets. The real, implicit value is business first, then customers. What should they do during similar situations in the future? Most likely, they’ll wait for you or another leader to make the decision.

Startups require compromise and quick decision-making. But whenever you’re tempted to act against your company’s principles for expediency’s sake, recognize what you’re risking.

Lesson 4: Proactively pay your debts

Engineers know that when you write scruffy code, you create technical debt. Like financial debt, this has to be paid down at some point – usually by devoting development resources to refactoring the product to work more smoothly and reliably.

The truth is that this isn’t reserved for engineers – every function is capable of accumulating debt. Imagine, for example, that you’re looking to recruit a Head of Marketing but are struggling to find a great candidate. You have a choice to make: do you keep waiting for a perfect fit, or do you compromise?

Neither is a perfect decision. Startups operate in a state of extreme scarcity and urgency, and you usually can’t hold critical positions open indefinitely. But hiring someone that’s only a partial fit creates an organizational debt that has to be paid off at some point. And, like financial debt, the longer you leave it, the larger your bill can grow and the less flexibility you’ll have in the future.

For example, let’s say you hire someone suboptimal for the Head of Marketing role. For a few months, you’re relieved to have filled the position. But pretty soon, that Head of Marketing is devising the rollout plan for a new market, allocating budget, and hiring team members. If they’re not the right fit, there’s a good chance that rather than solving your problem, they’ll end up creating a dozen new ones. Digging your way out might involve unwinding the entire team.

Every company faces issues like this. Since we were building a super-app at Gojek, we initially incurred a lot of product debt. When we deployed a team to create a new product like food delivery, they’d borrow components from ridesharing and build on top of them for their own needs. This was debt that worked at the beginning when we only had a couple of teams, but over time, the different services in the app became less and less coherent. UI/UX varied depending on which part of the app you were in, creating an inconsistent and sometimes confusing customer experience. Eventually, we realized we had to repay the product debt we’d incurred, so we designed a live library of components that every team had to use. Anytime we changed the live library, it populated across the different product lines. It was a significant improvement, but we should have been aware of it earlier and tackled the problem before it became so pronounced.

Ultimately, it’s inevitable that your startup will take on technical, operational, and product debt. The important thing is to stay on top of it. Have your teams catalog the debt they believe they’re incurring, and rather than reactively addressing it when crises occur, proactively create a plan to pay it down.

4. How Joel Greenblatt Uses Options– Thomas Chua

In his book You Can Be a Stock Market Genius, Greenblatt shares his secret to generating parabolic returns with a long-term options contract—Long-Term Equity Anticipation Security (LEAPS).

(On using LEAPS) “There is almost no other area of the stock market where research and careful analysis can be rewarded as quickly and as generously.” — Joel Greenblatt

Greenblatt would purchase a call option—which is the right to buy a stock at a predetermined price for a period of time. For example, we could buy a call option on Facebook that gave us the right to buy its stock at $300 per share by Jan 2023, approximately 2 years away…

…Typically, when we buy a call, we are bullish that Facebook’s stock price will go beyond $300. To buy this call option, we need to pay a premium of $45.

If Facebook’s share price goes up to $390 in Jan 2023, we would make 100% on our investment within 2 years. With an initial capital outlay of $45, we would reap a profit of $90 by exercising our call option, buying Facebook at a strike price of $300 and selling at a market price of $390.

But of course, risking $1 for $2 in returns is never a good investment from a risk-reward perspective.

If Facebook’s stock price trades below $300 in Jan 2023, the call option will expire worthless. For example, if it trades at $250, you would rather purchase from the market as opposed to exercising your right to buy at $300. You would rather let the call option lapse and lose the $45.

For Greenblatt, buying LEAPS call options makes sense only when there is a good chance of an event that will propel the stock price upwards significantly.

In Dec 1992, California was caught in one of the worst real estate recessions and Wells Fargo had the largest concentration of real estate loans in California.

During that period, many doubted if Wells would survive the real estate downturn and as a result, its stock price fell to $77.

Greenblatt’s thesis was simple_—_adjusting for cash earnings and one-time expenses, Wells was earning $36 per share before taxes. If things weren’t as bad as they seemed and returned to normalized levels, Wells’ loan-loss provisions would probably be $6 per share annually. This would translate to a normalized pre-tax earnings of $30 per share, or $18 after tax (assuming a 40% tax rate).

Conservatively giving it a price to earnings (P/E) multiple of 9 to 10 times, Wells could be trading at $160 to $180 per share (versus its price of $77 at the time).

Greenblatt determined that while Wells was embroiled in one of the worst real estate downturns, its financial position was actually quite strong. At first glance, Wells’ non-performing loans were huge, coming up to approximately 6% of Well’s total loan portfolio.

But lo and behold, these “non-performing” loans were actually bringing in a yield of 6.2%.

This was when the bank’s prime rate (the interest rate paid by the bank’s best customers) was 6% and the cost of Wells’ money (the interest paid to depositors) was 3%.

Non-performing loans are loans that are substandard. These include (1) loans that do not pay interest, (2) loans in which the full interest obligation is not paid and (3) loans for which it is anticipated that future interest charges and principal payments might not be paid on time.

Wells was being so conservative that 50% of its non-performing loans were still paying all the required interest and principal payments on time.

In other words, the most worrisome part of Wells’s loan portfolio was still earning a return of 6%. There was a good chance Wells would be able to recover a good portion of these non-performing loans’ value…

…Banks are a different animal from most companies. It’s difficult to assess what makes up its loan portfolio. The financial statements only provide a very general overview of the bank’s assets.

Although Wells had been conservative and their financial strength certainly looked strong enough to withstand this recession, there was still a small chance that the bank’s loan portfolio could make the investment go south.

Investing in LEAPS is a great idea when the risk/reward ratios are in your favor. LEAPS lowers the capital outlay and magnifies your returns.

For Wells, there were two likely outcomes:

(1) Things were not as bad as they seemed, and Wells would trade above $160, or

(2) The housing crisis would worsen and Wells would trade significantly lower than $77.

Based on Greenblatt’s assessment, (1) was significantly likelier than (2).

And two years was sufficient for Greenblatt’s assessment to prevail—if things weren’t as bad as they seemed, Wells was likely to trade above $160 within two years.

5. Arena Show Part II: Brooks Running (with CEO Jim Weber) – Benjamin Gilbert, David Rosenthal, and Jim Weber

When the CEO Jim Weber took the helm in 2002, the company was losing $5 million a year. It was $30 million in debt. It was a week away from missing payroll and the board was having weekly meetings to figure out how to make payroll.

It was a business of pretty modest size. It was a $60 million revenue business. When we talk about this revenue number, it’s not SaaS numbers. There are extremely real costs and making shoes, so you can imagine not making a ton of money or actually losing $5 million a year. That business had been around for 90 years and it sold all sorts of products at every price point, to frankly, a pretty random set of consumers in every category, not just running.

Enter Jim. Jim came in and vet the company exclusively on serving active runners as a segment, and he cut all other business lines. Over the last 20 years, he’s grown the business to over a billion dollars in revenue, a billion with a B, and well over a billion, is thriving, and thrived even through the pandemic.

Along the way, Brooks was acquired by Berkshire Hathaway and Warren Buffett personally elevated Brooks and Jim to make the company a direct report to him. Jim is a leader, a visionary, and a fighter not only growing the business over the last 20 years but personally fighting and beating cancer…

…[Jim]: There I was and I joined the board at Brooks, I joined the board at Nautilus, which was formerly Bowflex. I did some banking work, middle-market M&A, marketing companies to investors. On the board at Brooks, I had an inside view of what was happening there.

A good friend of mine, Helen Rockey, had run it successfully in the 90s, but she left. It was owned by J.H. Whitney Capital, really a top-notch for my money middle-market M&A firm or private equity firm and they bought it. The partners had left, Helen, the CEO had left Brooks, and it started to go sideways. New partners at Whitney, all new management, they went through three CEOs.

David: You were on the board the whole time?

Jim: I was on the board. I had a look inside and it was a crisis. You guys have experienced this, the weekly board calls on Fridays, the bank is not going to fund, they want more capital. It was exciting, as they say. After a couple of months, we did a lot of work, I saw an opportunity, and I jumped in. I love running businesses, I love solving puzzles.

I started telling Brooks, I really wanted to play the long game. I wanted to build a brand. The TAM—I love your industry—market and running is the biggest category in all sporting goods. It’s the biggest category and athletic footwear. It always has been. It’s about a $30 billion category globally, apparel and footwear.

All we had to do was get it and we could survive. We just kept at it by design because I just decided I want to play the long game and build a brand, build value, so that’s why I’m still there. I’m a weird duck, but I’ve had four owners and I played through each one and kept that opportunity out there for the next owner.

David: At that moment, though, Ben mentioned you did a little of this, a little bit of that, like a deadline in Wayne’s World about I’ve got a collection of hair nets and name tags. You were making football cleats? What was Brooks at that point in time?

Jim: Every brand in athletic footwear and apparel plays the whole athletic directors purview. You’re in every sport. What no one understood that I found out later is the mindset in our industry literally came from owning a factory.

When you had a shoe factory, you had to keep it busy all year long, and keep the people in place. So you went from baseball cleats, to wrestling shoes, to bowling shoes, to running shoes. You had to make everything, and business develop that way.

David: You had to view it as the product you made was like a factory that made shoes.

Jim: Most of it, we were losing money on and that was the secret. We had good, better, best, $30 shoes, $80 shoes, and then performance running shoes that really started at that point about $100. Then we had court shoes and family footwear. We call them barbecue shoes and learn more shoes because that’s what you did in them.

All of it was very low margin, all it was tying up inventory and cash. The retailers were ambivalent about it because we were number eight or nine and everything. Our brand was not strong, but when we made the decision to burn the boats on everything but performance running, the industry had never seen that before and most people thought we were crazy that we wouldn’t survive.

Ben: You came in as CEO, I think in 2002, maybe late 2001?

Jim: April 2001.

Ben: Okay. Was Whitney looking for you to do the thing that you had done several times in your career before, which was just get the business to profitability? Or did they have a notion that you had an inkling that you could build a big, powerful brand here and actually build a tremendous growth business?

Jim: By this time, I understood what they needed. I talked about a little bit of my book, I’d run three and I was a little bit smarter, fortunately. They had to liquify; there was no question about it. They were going to sell and the employees knew that I was just coming in there to sell this thing.

They had a pool on how long I’d last, but I wrote on my board one of my favorite quotes from Benjamin Disraeli, “The secret to success is constancy of purpose.” I wanted to create value. I want to build a brand.

I decided when I walked in, I was going to play through Whitney. I was going to get them a good outcome, but I was going to stay and play through it. I thought we’d get another private equity player, we didn’t.

The Whitney partners, Peter Castleman and Paul Vigano, I’ll never forget the meetings. They said this thing is kind of a mess. We didn’t know what we bought. You have to pick a path and go. It might take you five years, but you got to do it. In Brooks’ darkest hour, they wrote a check and recapitalized it. […] cram down, but they wrote a check and that’s when I came in. They were fantastic partners for Brooks, and we got them liquid.

The pitch I made to our team (and it’s what I believed) is that companies with issues get sold, companies with opportunity attract investors. I said, we’re going to have to park cars in the parking lot. We’re going to attract somebody. That’s the mindset we had. We were going to sell the future, not just sell the current.

Ben: If I’m remembering right, Whitney put in $7 million.

Jim: To recapitalize it.

Ben: I think that’s the last time Brooks has taken outside capital.

Jim: Absolutely. We saw a higher margin business and we benchmark against all the public companies. We’re asset-light, it’s really an inventory and receivables business, and there’s a reason we only have one store at our headquarters. We think it’s an advantage for us right now in the development of our brand. But if you have high margins and good flow through operating profits in the teens and you’re incremental, obviously capital, you can flow cash growing 20%, 30%, 40%. We haven’t needed dollar of capital since 2001…

…David: I’d say that’s a good business. Can you just walk us through how the economics of Brooks work?

Jim: Here was the insight that we saw. Monopolies are great, network effects are great, all those things are great. What I saw in Brooks was a book that was meaningful to me when I was at Pillsbury, the PIMS Principles. One of the highest ROI businesses were lower price point consumable items.

If you’re buying a Boeing jet, or a $600 wakeboard that never wears out, or an $800 golf driver, that’s a discerning purchase. The margins on equipment tend to be lower. But the titleless golf ball is a consumable for me anyway. Running shoes, for a frequent runner, will put 20–30 miles a week. They’ll go through 2.6 pairs of shoes a year. There’s the stickiness.

If you can earn a frequent runner that the shoe is really important, it’s a piece of equipment for them, you don’t have to resell them every time. You’ve got some stickiness there and you start to build customer loyalty.

David: Your average selling price for a pair of shoes today is $130 times 2.6 per year and a loyal Brooks customer stays with you for?

Jim: We had to earn them. There’s no guarantee. They’re curious. There’s lots of new innovation. They’ll try some different things. One of my favorite stats for our brand is shoe count at marathons because it’s a piece of equipment. You don’t want to be injured, you want to have a good experience. So we sponsor. Boston just happened, an incredible race. We’re always the number one or two shoes of course, that’s the punchline.

Ben: Do you have people at the big marathons counting?

Jim: It’s so good. They have high speed cameras, AI, they link it to the bib. They know exactly what shoe 20,000 people are running on, the model. It’s so cool. Houston Marathon, 6000 marathoners, 12,000 halfs. Number one shoe in the half is Brooks. Number two shoe in the fall, there was a little brand down in Portland, Oregon, they were number one. We are on their heels. That shoe count is a true test because that’s the frequent runner and it’s a piece of gear in that. The leading edge for us is to earn that customer and have their confidence.

Ben: All right, David’s doing the thing that I normally do and jump ahead and try to unpack the business as it is today. Let’s go back to the story. It’s 2002 through 2006, let’s talk about this era. You’ve made this bet where you’re going to shed every other product that you sell and you’re kind of going to piss off a lot of your channel because you know what sells really well at these big box stores. Those are your barbecue shoes. Can you take us to one or two of the key moments of the hard part of the decision to drop product lines that weren’t about frequent runners?

Jim: I think that the key to Brooks is that we knew we were going to have to build the brand at the runner level, literally a pair of feet at the time. So many retailers told me, Jim, we are not going to build your brand. We’ll try it, we’ll test it. We were tested at Dick’s Sporting Goods, I’m not kidding for 10 years. Twenty stores, 80 stores, 20 stores, 80 stores. You have to build the flywheel in these franchise products. That’s how running works.

The best-selling running shoes continue to be the best-selling running shoes year after year as long as they sustain it all around the world. We have two of the best-selling shoes now in the United States—the Ghost and the Adrenaline. They’re the two top shoes in the performance-running category.

When we go to retail, the biggest customers are the Big 5. It’s a fine sort of mid-price sporting goods retailer on the West Coast. We were doing $10 million of $60 million in revenue with them at $30 shoes. My first meeting with them was we love Brooks, we see a great future for you.

Ben: One sixth of all your revenue is coming from their stores?

Jim: Yeah. They saw our opportunity in 1999. I was losing money at $30. I couldn’t run fast enough from that meeting, because we left and we generated $5 million in cash by getting the inventory out of it. Those are easy decisions to leave those retailers and then we had to build it in the specialty-run community, pre-Internet, pre-ecommerce, which is a huge part of our business now that is sporting goods.

Ben: They didn’t want to sell your $100 shoes. They wanted to sell $20…

Jim: They didn’t have the customer, they didn’t have the runner. They had family athletic footwear at those price points.

David: At this moment in time, where was this in the running-as-a-sport market of marathon. Were they where they are today? Where are they on that journey?

Jim: They were on that journey. This was what we did at Brooks. I think we were the first one to identify that the real business was in trainers. It wasn’t in racing shoes, it wasn’t in spikes. It wasn’t in marathon racing shoes. The business is in the trainers.

We don’t sponsor college programs, they’re kind of owned and wrapped up. A lot of the college athletes that race in the big brands train in Brooks everyday. The business is trainers.

When we came in, we were humble and we were getting the business that we could. We had shoes that were really more back-of-the-pack people. They weren’t the fastest people. They’re support shoes and motion control shoes. People that needed functional footwear.

We’ve moved ourselves to the middle and the front, we’re trying to serve every runner. The insight was the sport is the soul of running. Track and field, cross country, road racing, the Olympics, now trail and Ultra, but the business is people that are investing in themselves—fitness, health, and wellness.

There’s no other sport that has that dynamic, where it goes from a sport to a pursuit of investing in yourself. We’ve always positioned ourselves right in the middle of that. We’re basically about you and your run. We’re not about the podium. We’re not about the tape.

In our sport, unlike basketball, everybody knows all the kids especially know what Steph Curry plays in. Most people don’t remember who won the Olympic Marathon and moreover what shoe they were wearing. The truth of matter is everybody’s unique, the shoe really matters, and you all know if it’s comfortable, if it’s working or it’s not. And frequent runners really do.

That’s the insight. I think we’re the only brand that is consistently executed against that. Every product we make starts with your biomechanics, your habitual joint motion, and what your needs are, and we’re all essentially different. We’re the only brand that begins there. And we’ve done that for 20 years now…

…Ben: Revenues going like this intentionally. You’re the fourth CEO. At this point, how do you get the team on board with these crazy decisions you’re making when there are three other people came in here and tried to turn this thing around and didn’t?

Jim: I think from a leadership standpoint, the real puzzle in that first year was gaining trust from everybody that mattered. BMA was our bank. It’s kind of a lost cause, we had to replace them. They just weren’t going to buy it. But Whitney invested—that was the key—and we kept them with us all the way through.

The leadership team took time. You had to deliver sort of an outcome, but here’s what we did. Six weeks in, we redid the plan, took profits down. The plan was millions of dollars. They didn’t have a prayer to hit that. We took profit down, but it was a profit plan. They hadn’t made a bonus in four years.

We went after cash flow. That was shrinking the mix. We had our plan that year and people got a bonus. We hit the plan that we’d sent nine months earlier. I spent really eight weeks intensively looking at it, but I think we knew what we’re seeing. We generated $10 million of cash that first nine months. That’s how much we shrunk the balance sheet with focus.

Here was the key, though. You have to do Horizon 1, Horizon 2, Horizon 3. You’ve got to solve it all. I had 10 things to do. The board said, oh, my God, you’re crazy. Pick four. No, you don’t understand. We had to get the Adrenaline right because that shoe was critical for us.

We had to refine that shoe in 2001 for 2002, and we got it right. The fourth Adrenalin was an incredibly balanced shoe, had a multi-density stability technology in it, super balanced. ASICS started to not deliver, and we ran. We air-freighted 1 color, 18 months cycles. It saved the company. We had to finish that shoe in 2001 to deliver on 2002.

David: You guys are like a semiconductor company.

Jim: At Brooks, everything’s complicated. Everything’s competitive, but it’s like moving a wall of bricks forward. I think as a CEO, you got to move it all forward. When some things are falling behind, you got to get those up. You have to deliver the whole business model.

You have to do it sequentially over seasons in our business because if you come to market with a ho-hum product line, you’re going to shrink that year. The lead times in footwear, it’s not the car business, but it’s more like the car business than the t-shirt business.

There’s tooling on everything, 12 sizes men’s, 12 sizes women’s, widths, colors. It’s scaling these things. In fact, there’s a lot of tooling. It takes a half a million to a million dollars to bring one style to market. It’s a lot of tooling and inventory…

…Ben: If David and I were on Zoom with you, we would be getting ready to enter hour number two and try to talk about every year all the way through. Tonight, I want to focus on how you came through the pandemic and some of the unique ways that you early realized, running actually was going to be something that people started focusing more time on and you were able to kind of lean into this new behavior. Talk to us about March 2020 and how you paid attention to what was changing.

Jim: A couple of big advantages. First was literally an obsession on runners. Participation links to unit sales and volume. No other brand has that clarity because most of the products in the athletic footwear industry don’t ever go for a run, or play basketball, or really even go to the gym. It’s casual family lifestyle footwear.

There’s nothing wrong with that. Some of those businesses are great. But we had an advantage because 90% of our products went through a retailer. That’s the problem. Europe retail shutdown in one week, then all of retail rolled through North American.

By the end of March, not a store was really open. That’s the problem. Cash cycle froze. Oh, my God, nobody knew it was happening. We didn’t know how lethal this virus was, how transmissible, and so on and so forth.

It was white knuckle time and we were there with everybody else. Everybody can write a book on that, but here’s what we did. We saw phases because we’d seen during the recession, running is a bit recession-resistant. We saw that in the Great Recession.

David: I was thinking about that.

Jim: Because it’s cheap and it’s convenient, all you need is a pair of shoes.

David: It’s like the healthy alcohol during a regular recession.

Jim: Thank you. We were not an essential business. Marijuana and alcohol were, so figure that out. But during the Great Recession, 50% unemployment in Italy and Spain under the age of 30, running took off double digit growth after the Great Recession.

We’d seen that before and it turned out to be Covid-friendly. You now know the story. It was social distancing friendly, outdoors, walking, hiking, running all made the cut, but nobody knew that. We had an hypothesis. We created this frame on how we thought running would recover.

Here’s what we did. First of all, Strava data magic. Every day after the quarantine shutdowns, Strava activity was growing and they were sharing that. Then what we did, we have 40 in the US alone, 45 field marketing people, we put them in high traffic running parks at 4:00 PM every afternoon and they counted runners. Guess what? It was growing every day.

We watched digital sales. We have visibility on 85% of our retail sell through. Digital went from 30% of all of our products going through a website of somebody’s, ours, or another partner’s. It went to 80% by the end of April. We sold more in May 2020, almost all through digital than we did in May 2019.

Running made the cut. We grew 27% in 2020, that Covid year. We saw this was the key because of our customer obsession and our ability to work. Multichannel was a big advantage in that time because we can move inventory around and make it happen. Inventory, if it isn’t there, you can’t sell it.

Multichannel was a big advantage. The other was our focus on the runner. We turned our supply chain on at least 6–12 weeks before anybody else did. Because if you were a broad-based retailer, there was no clarity on when the customer was coming back. For a lifestyle product, nobody went outside for a year.

Ben: Was the fact that you exclusively made performance running gear gave you the confidence to flip it back on? Because if you’re making all kinds of stuff in your factory and you’re pushing all kinds of stuff through retail channels, most of it is not going to sell, so you can’t actually open.

Jim: That’s right. Apparel and footwear inventory is life and death. You’ve got to manage inventory well. Because if you have too much, you’ll ruin the next cycle of inline product. Inventory is really critical, but we managed and played that cycle really well. We grew to 27% in 2020. We grew 31% in 2021. We would have been up 40% if not for supply chain.

Ben: What did you end up doing in revenue last year?

Jim: $1.13 billion. Great year. We cracked a billion. The billion dollar club is actually a rarefied club. There are probably maybe two dozen, global. Chinese brands are there now. It’s a great club to be in.

What makes us unique is it’s all premium, full price, full margin product. Most of the other brands have good, better, and best. Those are retail-driven merchandising strategies. They’re not really consumer-driven strategies.

Ben: Normally, we talk about seven powers as we drift into analysis here. You’re a Berkshire business, so we’re going to talk about moats. What is Brooks’ moat and how do you think about defending the castle now that you have what you’ve built?

Jim: We think a lot about it. I think there’s also something I’d add to that. Part of the moat can be business models. Business models can be really powerful. One of the things you can do as a company to create defensive moat structures is business model execution at scale.

We now are executing retail partnerships with the best retailers for running gear to runners at Super Jock ‘N Jill in Seattle, Fleet Feet running down in (I think) Menlo Park. Obviously, some of the better sporting goods players and outdoor from REI to Dick’s Sporting Goods, we’re their number one brand.

We’ve earned that over 20 years and we have deep, broad partnership programs with them. Digital marketing, consumer journey, runners are digitally savvy. They’re obviously all over the web. They start their shopping experience there.

We reach them in active evaluation mode. Once you start looking at shoes, if you don’t see our ad, I don’t know how we missed you. We’re spending a lot of money at runners now, maybe more money at people who run in active evaluation for running shoes than any other brand. Very focused. That’s not easy to do in our industry at scale.

I would say this is our moat. I think runnability, fit, feel, and ride, there’s a lot of good shoes out there. It’s actually not easy to make a great shoe. Anthony Fauci made a joke about shoes. “Vaccines are tough, they’re complicated. It’s not like making shoes.” We get a lot of that.

The refinement that goes into mile and making mile 26 acceptable, is really big. I think great product is not as common as you might think. The people on the inside, the frequent learners know. I think you always got to lead with product. That’s the first brand experience, product experience.

I think we do some hard things. We build a great product consistently, year in year out. It fits and it rides well. Then what we do on the retail side, partnering, activating in real life, running and selling shoes in real life events, and all the like, we do that better than anybody else. We service them. We deliver on time, complete. The digital piece, we’re excited about it. We’re still just getting started there, but we’re really focused on it.

David: I’m curious. I hadn’t even thought about Strava and the amount of data that you’re able to see from that. What does the digital side of running in the future look like for Brooks and for the industry?

Jim: It’s interesting because quantified self and those tools have been ubiquitous. They’re out there. The Apple Watch is a damn great product. What’s interesting about that is both Under Armour and ASICS have spent hundreds of millions of dollars on digital apps. I think they’ve really struggled a long time.

David: Runkeeper and MapMyRun both.

Jim. Exactly. I wanted to buy every one of those and Warren wanted me to do the multiple on EBITA. There was no EBITA. Let’s just say it’s hard to do acquisitions sometimes.

David: At least one of them was a completely free product, I think, right?

Jim: Oh, man. They don’t make money. Under Armour is trying to sort through that now. They’re starting to shrink, so as Adidas. Those tools are really powerful for data, but how do you monetize it? We haven’t gotten there yet, but we’re building a Brooks Run Club. Finally, we’ve launched.

It’s not a loyalty program, but we want to engage our zealots. We want to engage our true believers. The data piece of that is going to be key. We want to come up the kinetic chain and find a sensor system and a data capture system that can get to your biomechanics as you’re running. Because what happens is, if you run a marathon, your gait in the last 5–10 miles really degrades. And that’s where injuries happen.

We’re doing a lot. We have a lot of partnerships. We’re really trying to figure out how we get good runner data in real life, not just in the lab. In the lab, we can test everything, but we want to get out in the wild.

David: Do you think you need to do what the other folks in Oregon have done and build the whole consumer experience yourself? Is it a partnership?

Jim: We’re going to build it and we’re going to partner, too. Nike Plus is a fantastic ecosystem. It just is. I’d love to have an ecosystem like that. But we’re still selling more runners than they are.

We became the number one running shoe brand in the United States in the last 12 months last month, 21.5% share from performance running. We know where the battles are. I think one of those powers is we make money on that. The digital space, there’s a lot of carcasses there, but we’d love to have it, and we’re going to work on it…

…Ben: Yup. All right, one closing topic. You battled, survived, and beat cancer while building this incredible business. How has that changed your perspective on leading on the way you spend your days and on life broadly?

Jim: Let’s close it on a light note. Let’s talk about cancer. That’s the takeaway for these wonderful people. I didn’t expect it. It came out of nowhere. Unlucky. How did this happen? Esophageal cancer, I just felt awful. My worst running experiences I’ve ever had and I got the diagnosis. Chemo, radiation, surgery, complications in surgery, another surgery. but the good news is I’m cancer free. I think it’s gone. I think it’s out of my body. The bad news is I’m even slower and I’m kind of a Frankenstein in my systems, but it works. Everything works.

I think what I learned from that, though, is that every time I have a friend or a family member who gets cancer, I go to the web. You look at it, understand it, and what the treatments are. They always give you a five-year survival rate. My five-year survival rate was 20%, one in five. My five years is this November. Someone has kick its butt.

What I quickly figured out and I talked it through with my family and obviously with Warren, frankly, is that I decided that I was doing exactly what I wanted to be doing. I love what I’m doing. I’ve got family, I’ve got an active lifestyle, I’ve got this fabulous brand and a company that I’m a part of, and a team. I just love it. I don’t know what else I do, which is a problem.

I decided I didn’t want to live in fear. I didn’t want to live every day thinking about what I had to lose. I had a lot to lose. I didn’t want to be bitter about why me. I just decided I want to soak in everything I can on any given day. I want to be a CEO, I want to be a dad, I want to be a husband, I want to be a papa. I’ve got four grandkids. That was it.

I think for me, that was really powerful because I don’t want to be that cancer guy and they brought it up. It’s just not my thing. I’m glad to talk about it. I don’t hide it. I’ve learned a lot. I want to enjoy the things in life I really enjoy.

That’s where I learned, but I think everybody’s different. You do find out companies, when you hit challenges, you learn what you’re really all about. I think it’s the same for people, of course. I feel really lucky because I’m doing what I want to do. Cancer is in the rearview mirror. It’s good.

6. Martin Casado – The Past, Present, and Future of Digital Infrastructure – Patrick O’Shaughnessy and Martin Casado

[00:03:58] Patrick: How would you put chapter headers on the stages of cloud adoption, going back to, I think, Azure and AWS, are sort of mid-2000, 2005, 2006, thereabouts, relatively speaking, a short story. What do you think the major eras of the cloud story have been so far?

[00:04:15] Martin: Right before the cloud, of course, everybody ran their own internal IT. Right? And so they kind of write their own servers and their own wiring closets. The cloud showed up and the early usage was what you would typically find in a technology early adopter ecosystem. It’s more new projects and startups and hobbyists, the average workloads were relatively small. There was exceptions to that of course, like Netflix is a very famous one, which went all in the cloud very early. But in general, that was what it was. This is like 2005-2010 timeframe and still was very experimental. A lot of the time there was big discussions on whether the enterprise would actually go into the cloud. When I ran network and security for VMware, which is 2012-2016 timeframe, I think that was the more mainstream adoption of the cloud. You saw large organizations, traditional enterprise moving workloads to the cloud, very serious discussion with the Fed and the government. It became a mainstream way of doing things. If you were a large organization and you didn’t have a cloud strategy, I mean, you were either considered a laggard or a special case. That brings us to 2018-2019, and now we’re seeing a shift where the move to the cloud has implications on your finances, because now instead of you being able to buy a physical asset and internalize that, you’re basically paying a portion of your income to a third party.

Now there’s a lot of discussions around, how do you optimize the use of cloud? Is the right thing to go all in on cloud? Is it something that you do a portion or whatever? I just want to make one quick analogy, which is, I always view companies going in three stages, the product stage, the sales or growth stage, and then the operation stage. The product stage you’re finding product market fit. The sales stage is you’re getting to repeatable sales and growth. You don’t really worry too much about unit economics. And the operation stage is when you care about unit economics and you go into multiple products and you do all the operation of complex things. The cloud had gone through the exact same three phases, which first was trying to find product market fit, which tended to be within new projects, funding the projects. Then it went to the growth phase where everybody went all in and didn’t worry about the implications to the economics of business. Now we’re at the operations phase where we’re starting rationalize all of that.

[00:06:26] Patrick: Maybe tell the story of Dropbox, which I think as an individual company, is a great example of cloud isn’t just some panacea. It has incredible benefits in terms of how quickly you can get going, outsource the reliability to somebody else that’s just focused on this, AWS or whatever. But from a cost standpoint, it can get really out of hand. I think Dropbox is a good and probably unfamiliar to most tale of going the other direction.

[00:06:49] Martin: There’s basically two trends that happen at the same time. It’s important to understand those two trends to understand what happened at Dropbox and actually a number of other companies too, it’s not just Dropbox. The two trends are the following, the first one is cloud, which we talked about. The second trend is SaaS. And specifically what’s unique to SaaS is, is before if you were a software vendor, you would build software and you’d ship software, and somebody else would run it on their own infrastructure. Your COGS, your cost of goods as a software vendor did not include the infrastructure that it was being run on, because it was being run on somebody else’s infrastructure. For example, my startup, we built software for networking, we shipped it, other people would run it on their infrastructure. However, if your SaaS, if your product is software as a service, then part of your cost of goods is actually the infrastructure. Someone comes, says, “I’ve got a SaaS site and someone comes and uses it, then they pay me some, and then I pay say, AWS a portion of that.” That is a change of cost structure. The books look very different.

While the cloud is getting adopted, all software is going from basically on-prem to SaaS, and in some cases, and there’s many of these cases, it turned out that it was very tough to get software margins just because the cost of the cloud services on the backend was so high. The era of shipping software, we’d all say these companies have 80% margins because you basically write the code once and then it’s free to copy bits, so you just ship it to everybody else. Especially in infrastructure, there’s many companies that felt like they’re basically reselling a thin layer on top of AWS or one of the big clouds, and then paying a large portion back to them. For example, I know multiple companies that are household names, where they’ve got product lines that have 0% margins because all of the money goes back to the cloud services it’s hosted on. Dropbox very famously had this situation where S3, which is the storage layer on Amazon is not optimized for this use case of many small objects. They found that they were paying a tremendous amount. Now, they were a very large user of this specific use case. AWS was not optimized for it. They decided to build their own internal infrastructure and probably saved the company at the time, by moving off the cloud and taking it internally…

...[00:10:08] Patrick: There was a really interesting thing that you wrote about the interesting concept of lost market cap of companies that were big users of the public clouds. I’d love you to walk through that concept, because you mentioned maybe this saved Dropbox, the company, and I get that that’s a very special, specific case, but it sounds like there’s a bigger story here of lost margin and therefore lost market cap because of the use of public cloud. I’d love you to walk us through that.

[00:10:32] Martin: We did this analysis, a very simple analysis, which we said, “Okay, right now there’s a tremendous amount of money that SaaS companies spend on cloud.” Let’s say if they brought it inside and they were able to drop those costs by half, which most people agree that you can drop the costs by half by bringing it inside. If you could do that, what would that do to the stock price? Normally when people look at this problem, they say, “Well, if you bring this inside, yes, it’ll save you money. You’ll save 50%, but that money won’t cover the team, the complexity, because that’s not a lot of money.” But if you look at the leverage that increase in margin does to the stock price, now you can free up for a large company, potentially a lot of money, which will flow over to cash, so it could be a big win.

What we learnt is that we looked at just public software companies. We looked at 50 of them. We looked at all of their spend and we said, “Let’s assume you cut that spend in half.” Then we calculated their margins. And then we said, “Benchmarking against other public companies, if their margins were half, what would that do to the stock price?” It turned out that it would increase in aggregate the stock price by $200 billion. Just a tremendously high number. I think we wrote $100 billion to be conservative in the actual blog post, but $200 billion. That means if you’re a company that’s say, worth $10 billion, and you can reduce your COGS by a bit, you could now become worth $14 billion, and then you have access to that for debt and hiring or whatever else. Because those two trends happened at the same time you had the cloud trend, as well as the SaaS trend, I don’t think there had been a lot of focus on what it does to the margin structure. We did the first analysis and said, “Actually it’s huge and it can impact your stock price.” I do think, especially now in this market correction, it’s a good thing for companies to start looking at…

…[00:14:03] Patrick: Before we get to something like Kubernetes, a little bit more complicated of a topic, I’d love to just return to super basics around digital infrastructure in the first place. And maybe even go all the way back to the original AWS website, where I think it was storage, compute, database. You mentioned networking. What are the base level, most primitives of the digital world? What are the most important, big things that actually happen? Because I’d love to understand what’s changed in those areas, like compute sounds like compute. What is changing in those three, four, five base level areas?

[00:14:33] Martin: The traditional infrastructure’s computing and storage, and then databases. Prior to cloud, you’d buy a server from whatever, Dell or IBM or HP. You’d buy a switch from Juniper or Cisco. You’d buy a storage array from whoever, EMC. And databases from Oracle. So all those have now been, basically, collapsed into a software layer over basically merchant hardware in the cloud. So you can get the equivalent of just compute by TC2. You can get very flexible networking layers, where you can put security policies and that’s largely implemented in software within the cloud. And then you get these scalable services, like the database services that are scalable because they’re in the cloud. And so that’s the bread and butter of the cloud.

For a cloud is basically you take these traditional abstractions, compute and storage, that were connected to a box and now they’re just basically software services that you can spin up and they should be able to grow to the size of the workload. But what has also happened in the last say, five years is a number of services then built on top of those that are higher level abstractions. So for example, machine learning workflows, analytic workflows, different types of databases that focus on different types of query patterns. I want to do analytics, or I want to do LTP, or I want to do very fast queries or time series. We have seen this renaissance of infrastructure, again, which used to be tied to a box now being implemented as a software services in a way that’s much faster than we’ve seen historically for that exact reason. That it’s not confined to a box…

…[00:17:30] Patrick: How will that happen? It’s like up against a death star or something. Like facing these three big companies. What do you think the best entrepreneurs will do? Pick something like crazy specific and just go after a single thread? How do you think this innovation cycle will happen?

[00:17:43] Martin: All of these companies are like, very strong repeat founders and the companies are Mighty, Fly Out IO and Mosaic. So, what do these companies do? So Mighty is browser as a service. I don’t know about you, but right now even as we speak, I probably have 30 tabs in my browser. My laptop goes slow. If you use Mighty all of that’s offloaded and you get this crazy good experience, which is great for most of us, especially as the browser gets more workloads. What is Fly? Fly allows any developer to run compute workload at the CDN tier all across the world, which is important if you care about responsiveness to the users. And what is Mosaic? Mosaic is, basically machine learning as a service. So they provide the ability to run models very quickly for AI specific loads. So, what’s unique about all three of these companies is all of them are doing their own hardware. They’re looking to run servers, they’re racking and stacking. And these are very, very strong founders.

All of them are repeat founders and all of these companies have great traction. So what is happening here? I think it’s exactly what we’ve spoken about, which is there just are across the industry certain workloads that, if you look at that very specific workload, the cloud is just not optimized for them. And that provides room for the Mighty and Mosaics and Flies of the world to provide something that is a very attractive proof point or performance point or whatever it is, with respects to the clouds. And so I don’t think the answer is we’re going to see a lot of drop boxes, where the end customer builds their own data center. I do think we are seeing very concrete signs of third party companies coming in and providing cloud services that are just at a much better price point, or a much better performance point, or much more optimized for a workload. And because the cloud is growing to size, there’s enough market now for solvent companies to do these. And so I think this is the very beginning, again, of a much bigger trend.

[00:19:33] Patrick: Can you say a bit about your view of what I’ll call API first companies? Which I think a lot of people would include in this definition of digital infrastructure. If I can hire Stripe to be my payments processor by simply inserting a API into my software that I build and care about. And then there’s one of these APIs that’s proliferating for kind of everything. What do you see happening here? Is that infrastructure in your mind? Where does this fit into this equation?

[00:19:58] Martin: As markets grow, the unit to which you monetize gets more granular. And my favorite example of this, and it’s one that may be a cliche but it’s worth saying, is the car market. So, way back when in 1913, Ford had a factor called the Rouge River Factory. And this factory literally went in on one side, it was like water, rubber and coal. You know, and like iron ore, and what came out on the other side was cars. And the reason is there wasn’t a sufficiently large market for cars to actually have suppliers. You couldn’t be someone that provided wheels or whatever. And if you look at the car market now, I mean, there’s companies that provide nuts and bolts and you’ve got multiple tiers of OEMs and integrators, et cetera, et cetera.

So the same thing has happened to systems historically. So in the 1970s, the same company would build literally the chip, the motherboard, the sheet metal, the operating system of all the apps. And then of course the OS got disaggregated from the hardware and then the apps got disaggregated from the OS. So now what’s happening is the application itself is being disaggregated. You take any application, you blow it up and assume the market for this application or any application is so big that independent component of applications now can become companies.

So what does an application do? I mean, applications authenticate users, they need access controls, they need to send emails, they need to do payments. These are things that all applications can do. So it’s almost like every help or library in an application is now becoming a company. So much so that I remember even five years ago, you drive up 101, the heart of Silicon valley in the Bay area, and you’d have billboards where the entire company was an API. PubNub, Sendgrid, you know, Twilio. And so this is a major movement where now you don’t have to build a business app to build a company. And for an infrastructure person, this is super exciting because most of the founders I invest in are technical founders that are providing technical functions that are only useful to developers. And in the past, it was hard to build a business that way, but now you absolutely can.

If you’re in tech at all, or you’re an investor at all, I definitely think you should look at an application and assume that any sub-component does have the potential to now become a company, because the market is just so large.

[00:22:11] Patrick: What stage of that process do you think we are in? Twilio and Stripe, everyone knows turns out payments and sending messages. It’s almost like the equivalent of storage and compute in application building. Where do you think we are in that process?

[00:22:25] Martin: I think we’re still pretty early. I mean, on average, an application uses 17 external APIs. I think like a mobile app, something like that. But if you look at the use of libraries and open source and everything else, it’s still incredibly high for people having to integrate external components and management operate themselves. I think that there’s still a long way to go, especially as we get into kind of more complex things. So for example, every application often requires some sort of internal policy. Who can access what, or you know? And this is a very specific computer science problem. How do you build a language or a policy language that kind of accesses, that allows a third party to declare a set of rules and mitigates access to those rules? Like, this is a component in most programs that can be pulled out and turned into a company. There’s a number of companies looking at that, that are just getting started.

[00:23:16] Patrick: When it comes to this developer facing tooling, there’s this open source way of building and there’s the more proprietary, closed source way of building. What have you learned about what works well in which domain? And then I’d love to also learn, like if you’re an open source company versus not, what is more or less important as you think about product and go to market and everything like that?

[00:23:35] Martin: I’m starting to be of the opinion that as we move to SaaS and that’s the primary way of consuming infrastructure, which it seems to be, that open source matters a lot less. And the reason I say that is, if I’m a developer and I’m writing an application and I need to authenticate my users and I need to authorize their access to things, and I need to send them emails or send them SMS texts or whatever, I have two options. I could download some open source package and then operate that, or I could just use an API that somebody else operates. The secular trend is I’m going to use the API that somebody else operates. And if I’m doing that, whether or not the code for that is open source, doesn’t matter that much to me. So let’s take the case of it is open source. So, even if it is open source, there is some value there. A lot of actual code to running that service has to do with the operations of the service. Like, how do you make sure that it’s high availability? How do you debug it? How do you check for performance? Like, and that operations code is to be very specific to the actual service running. So it isn’t even useful.

So that would never be open source anyway. So even if I had the source code, I couldn’t really use it and operate it in the same way that somebody else could, or is running it. When it comes to dev tools, things that I am specifically using in my program as I develop, like that will always be open source and that’s very important. But anything that’s functional and offered as a service, I think the actual value of open source decreases. And what raises importance is actually open standards, which is, I still want be able to make sure that I’m not locked in to one and I can move between them, but that’s not an open source argument. That’s kind of an open standards argument. And so the role of open source has obviously shifted very, very quickly in the last 10 years, largely driven by this consumption with SaaS. And I think that we’re getting a more nuanced view of where it’s useful and where it’s not. Whereas 10 years ago, there was this broad consensus that open source is great and it’s going to take over the world. And that just doesn’t seem to be the case in the way that we all thought…

…[00:28:41] Patrick: Going back to this notion of, so if they’re the consumers of these APIs or little pieces of infrastructure, I absolutely love the Ford factory example, and what happens as it matures, that it’s so clean. What do you look for as an investor when you are seeing one of these, let’s say API forward or first companies for the first time? What is your method of investigation? How are you processing a new company?

[00:29:03] Martin: So throughout this all together, we talked about a trend. So there’s a lot of frontend developers. We talked about probably 100 to everyone backend. And those frontend developers, they’re building more and more of the application. So in the past, they had to… Were very tied to the backend more and more. Instead of having their own backend, they can use it an API from a third party company. Let’s say they’re using 20 little SMS or whatever. The interesting thing about these API companies that offer to the frontend is that the unit of consumption really is like a function call or an API call. So they almost have these consumer-like dynamics. So the primary evaluation criteria, to answer your question, and why it’s so different, in the past, if you’re going to evaluate a server company, who’s the buyer, what’s the go-to market motion, what’s the ACV.

You talked to a bunch of the buyers, you’d see if they can build the technology, et cetera. Now, with these API companies, you literally just can look at what the usage graphs are, how many users, how do they monetize them, et cetera, and it’s become much more of a bottoms-up, or SAS, or consumer type profile. So we stopped a lot of that approach to investing when evaluating these companies. It’s much less about can they build it, who’s the buyer, and it’s much more about how they use it in a practice, then it’d very interesting. A lot of these companies, they do. They’ve got these beautiful growth patterns, just like you’re looking at the next WhatsApp. They really are almost consumer-like phenomena.

[00:30:27] Patrick: What would be the most common red flags or disqualifying observations if you’re investigating one of these companies beyond lack of that nice looking usage or engagement?

[00:30:39] Martin: Well, I’ll tell you what I’ve gotten wrong. I do come from the older era where you actually evaluate the technology, you have a thesis on go to market. Often, we’ve seen these companies come in and they’ve got these beautiful usage graphs. They haven’t monetized yet, but we’re like, “Oh well, who’s going to pay for this?” Or this is just developers, like whatever. And then we kind of talk ourselves out of the deal, because we know the market better than the founder. And in almost every case, I’ve regretted that because the reality is, and this is an internal thesis of ours, is the graph in almost every case is just smarter than our theorying. The market actually knows what it wants.

These days, if one of these API companies is doing very well and the usage is great, I’ll give you an example, Hugging Face is a phenomenal company. And if you looked early on at the usage, this thing is a rocket ship, and you can have a bazillion theories why you can’t monetize the model, and you have a bazillion theories of why their go-to market is going to work. But the reality is the market loves it, it’s a great company. For me, it’s almost like a counter thing, which is, I do think that this API makes life a lot easier. You don’t have to have a grand unified theory about how things work, because you can literally just look at how this thing’s being consumed, because the consumptions become so bite-size; you get a lot of early signals. I think it really boils down to…

[00:31:53] Patrick: It comes down to usage.

[00:31:55] Martin: Yeah, to usage.

[00:31:56] Patrick: How should these things be priced? What have you learned about actually building the revenue model around something that looks more usage based? All these examples, AWS, what we started with, these API companies, they tend to be usage-based pricing. So what have you learned about that? Is that the right thing? Do you think that changes?

[00:32:12] Martin: It feels to me though, apps are for seat pricing, and infrastructure is usage pricing, and that’s basically how it is. And if you’re in the frontend, you’re not doing usage pricing, you better get there. You really have to. And if you’re apps, and you can get away from seat pricing, that just seems like that’s where you’ll end up. I do feel that when it comes to company building, there’s a few areas where there’s no simple answers. There’s a lot of stuff that’s systematic, like how do you hire your sales force, it’s pretty systematic. How do you create your org is systematic. But one of the things that’s just not systematic is pricing. Pricing is actually dictated by the shape of the market and the shape of the product. And it takes months to get it right. I’ll give you three mental landmarks, and I think the rest is just actual work.

So one of the mental landmarks is pricing is often fixed by the market. And so you should look at the ecosystem and the other types of companies and how they price and I think you should follow that model. For example, if you’re building on top of Snowflake, how Snowflake charges is going to be very similar to how the customer expects to buy. And if you’re building on top of that, you’re going to want to align with that. And I’ve been in many cases where the companies wanted to innovate on their own pricing model, but the ecosystem alignment just wasn’t there. And it was just painful until they had to change. I think another mental landmark is the market will tell you the price over time, but not initially. The less that you have public or the less that you force your opinion on, the better it is. I do think that a lot of early sales discussions is just to figure out pricing, that’s what it is. Your goal is to reverse engineer how they think about that. The good news is because the consumption is so much higher on these, and the unit of consumption is lower, it’s per API call, there’s kind of a lot of room to experiment…

…[00:40:14] Patrick: And as you think about the ways that all of this intersects with the real world now, which we really haven’t talked about. We basically talked about digital infrastructure that leads all the way up to applications at the top end, and the APIs in between and all this great stuff. But it seems also like as we mature, more of this technology will apply to the real world too, whether that’s new kinds of hardware, whether that’s intersection with physical goods like cars. How do you think about that side of things and maybe the hardware world of technology?

[00:40:45] Martin: People have a hard time grasping what, let’s say, AI and ML concretely provide, because it’s such a diluted buzzword. So for everybody that’s listening, the important thing to realize is that what modern AI and ML does, which we’ve never really been able to do before in systems, is take unstructured data, and digitize it, and add it to the typical logic of the program. And we’ve never been able to do this with vision, like objects out in the real world. We’ve never been able to do this with natural language in the level of ASCII we can. We’ve never been able to do this with voice or speech.

That technology just hasn’t existed and so we’ve never been able to build the big programs around them. And now we can, and it’s a sufficiently different workload that two things happen. First, it pushes software into the realm of the physical world. We can now see things and interact with things. And we’re talking quantum leaps of accuracy improvement. It also drives the type of hardware and software that we build, because the workload is so different, right? So we’re seeing tons of innovation all the way down to the ASIC level. Mosaic as a company is building a data center focused just on this type of stuff. So I think that this really is a massive impact on infrastructure at large, not just the infrastructure, but also what sorts of applications software can go after.

[00:42:10] Patrick: It’s very cool to consider what all that might mean. I mean like self-driving cars is like the obvious constant example of what computer vision might allow us to unlock. Obviously, cloud had this crazy impact on the services you consume. It’s unlocked innovation by reducing friction. As you think about what’s going on in the digital infrastructure world, period, what are you most excited about in terms of what it might unlock in the 2020s or over the next decade that maybe we’re just starting to think about?

[00:42:38] Martin: Any problem that human beings go after that’s been outside of the realm of software is currently in the realm of software. And this is farming, agriculture, oceanography, you name it. And so I am a tech optimist and tech maximalist. I think that part of our job is to solve problems. It has really been limited to IT, like information. And now I go from IT to just tech. You look at any industry, any industry at all, and I think that it’ll be touched by this. That’s, to me, just tremendously exciting. What’s interesting, I would just say very quickly is we’re still asking the question. Are these still software companies or something different? So you could say, this is just software going after agriculture. Now you still have a software business, or you could say this is still an agriculture business, or you could say it’s something totally different. That’s a question I’m personally very interested in…

…[00:45:34] Patrick: If you put your investor hat on, I guess your purely selfish investor hat, meaning you were just trying to maximize returns, and you could somehow have a crystal ball that would reveal some information about the future, which is currently uncertain, where if you knew what the future was going to hold, be super valuable to you as an investor, trying to earn a return. What would you ask of that crystal ball? Like what would you want to know about the future that you’re not sure which way it will go?

[00:45:58] Martin: I am very curious about where crypto lands, and I think there are three potential views, right? On one end, on the most negative and barren folks are like, “This is all fake. It’s just Ponzi scheme, yada yada, yada.” On the extreme other end, it’s a total reformation, not just of technology and companies, but an organization. This is like everything. You don’t just have routers. You’ve got crypto routers. You don’t have just storage. You have crypto storage. You don’t just have businesses. You’ve got dos. You don’t just have money. You’ve got DeFi, like everything changes. And then there’s a bit of a middle view somewhere. This is a continuum which says, “You know what? There’s something very innovative there on the ability to build networks. There’s a number of primitives that are very innovative on the ability to build applications. There’s a number of innovations on how you offer new services to consumers where you don’t know the endpoints. There’s a lot of great primitives, consumption to monetization layer, just like social was primarily a consumption monetization layer.”

In that future, that layer is on top of a lot of systems, but you still have traditional computer networking and storage. You still have traditional clouds. You still have to know all of those things. And it’s something that’s added to that. And I think the answer to that question given the amount of money that’s already involved is enormous. And I don’t think anybody knows the answer. I tend to be in the middle where I think that there’s a real innovation there. I think there’s real value. I think it’s a real unlock for a lot of new business applications and use cases. But I think that infrastructure itself, a lot of the traditional models still applies. You still have to build databases. You still have to use storage. You still have to understand the trade offs of asset. A lot of these things still apply.

[00:47:41] Patrick: Obviously distributed systems. Some of the smartest people in the world are working in distributed systems, not necessarily crypto networks, but just like the ability to distribute state or update state constantly faster, smoother, or whatever. As an infrastructure person, when you look at the current technology in crypto networks, maybe the dominant three or four, what are you watching or interested by or looking at, the consensus mechanisms, the scaling ability? What are the dimensions that you as an infrastructure person are keyed in on today?

[00:48:10] Martin: The crypto origin solves a very important problem. That’s traditionally not been solved practically. And that is allowing basically an anonymous set of people with no prior trust relationship to have strong guarantees on something, right? Originally it was a ledger, and then it’s become more to generalized compute. That’s a very, very real innovation thing. And that unlocks very, very interesting business use cases like we’ve mentioned. But distributed systems is one of those things that you just can’t paper over with a thin software layer. You can’t hide it under an API. You’ve never been able to. There’s entire languages that just help programmers manage distributed systems. What’s important is what developers end up using, or what distributed paradigms they end up using, because that will drive the capabilities of the system. So if everybody says, “This is a purely distributed world and everything I write must be purely distributed,” that will have some implications of the type of systems that you can build.

So the thing that I’m most interested in as kind of an old distributed systems guy is what are the nature of the applications? Is this going to land in the realm of purely distributed stuff? Is it only embarrassingly parallel applications, like DeFi is an embarrassingly parallel application? There’s other things that are embarrasingly parallel. Or, is this going to go more to the model of general compute? Is that something people are going to do? Are people going to build like the AWS in crypto? The answer to that is actually very, very significant, right? You could say, “Well, listen, traditional distributor systems are great for building AWS, and this is going to just be the consumption monetization layer, or it actually is going to cause innovation in the way that we do distributed programming in the future.” I don’t think that’s clear yet where that’s going to land.

7. Watch: How Does a Dead Fish Swim Upstream? – American Physical Society  

Take a quick look at this trout swimming upstream. Notice anything unusual?

[Video of trout]

You’ve probably seen something similar countless times; the fish wriggles against the currents that push it backwards, slowly making headway until it turns and ducks out of the influence of the stream. Nothing special in that.

The only thing is, this particular fish is dead.

Yes, you read that right. No matter how lifelike it looks as it undulates across the tank, that same trout would just go belly-up if the current were switched off. So how can it possibly swim upstream?

A team of researchers from MIT and Harvard were equally surprised when they happened upon this phenomenon by accident. They’d been studying the way live trout conserve energy by swimming behind obstacles that block the current*, and unintentionally placed a dead fish in the experimental setup. When they took a closer look, they were stunned.

“It was incredible, very counterintuitive,” MIT researcher Michael Triantafyllou says, describing the shock he felt upon seeing the fish swimming upstream. He explains that while he knew trout were good at conserving and even extracting energy, he had no idea that they’d be able to extract enough energy from the surrounding fluid to swim upstream without expending any of their own energy. Immediately, the team started to investigate this new, seemingly impossible phenomenon.

As it turns out, objects that block the natural flow of water, like a rock or a boat, create a series of complex vortices in the current as the water navigates the obstacle. As anyone who’s tried to grab a fish knows, fish are quite flexible all down their spines, which allows the head and the tail to move independently of one another. In certain situations, the array of vortices forming behind an obstacle cause the body and tail to flap in resonance. This tilts the body in such a way that the vortices, which cause a pressure drop, apply a suction force that propels the fish forward.

As Triantafyllou explains, “You have a flow behind the obstacle, which creates a continuous stream of eddies. Each eddy contains energy and also causes the pressure in the fluid to drop… the eddy causes the body to flap back and forth, and the fish manages to extract energy.” Since all of the energy is supplied by the vortices, it doesn’t matter at all whether the fish is alive or dead, if the timing happens to be right.

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Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. Of all the companies mentioned, we currently have a vested interest in Alphabet (parent of Google), Amazon, Meta Platforms (formerly known as Facebook), and Zoom Video Communications. Holdings are subject to change at any time.

We’ve constantly been sharing a list of our recent reads in our weekly emails for The Good Investors.

Do subscribe for our weekly updates through the orange box in the blog (it’s on the side if you’re using a computer, and all the way at the bottom if you’re using mobile) – it’s free!

But since our readership-audience for The Good Investors is wider than our subscriber base, we think sharing the reading list regularly on the blog itself can benefit even more people. The articles we share touch on a wide range of topics, including investing, business, and the world in general.

Here are the articles for the week ending 22 May 2022:

1. Flexport: How to Move the World – Mario Gabriele

The name “freight forwarder” is strange. It’s the kind of term whose meaning seems literally evident but is blurred by a sort of tedium-cloud. It is the cousin of descriptors like “insurance agent” and “data analyst.” 

A freight forwarder is a tour guide for objects. Or, at least, that is how I have come to think of it after having it explained to me (and re-explaining it to myself after I have forgotten) by a series of patient people over the years. For, say, a shipment of pillows to make its way from Taiwan to France, it must move between ships, planes, trains, and trucks. As many as twenty different companies may be involved in a single shipment, each handling one stint of the multi-modal journey. Critically, each party is incentivized to care narrowly about their leg rather than the entire trip.

The freight forwarder sees this salmagundi of boats and warehouses and flight maps and says, Don’t worry, I’ll take care of it. With the savvy of a good tour guide, it helps the customer navigate the mess, keeping the itinerary, forewarning chancy routes, bum ports (the “bad neighborhood” of logistics), and charting an optimal path. They are the concierge of conveyance, consiglieri of transit.

As it turns out, this is a big business. The global freight forwarding market is pegged at around $182 billion with projections to reach $221 billion by 2025. That amounts to a modest compound annual growth rate (CAGR) of a tick below 5%.

This is also an extremely fragmented space. As of 2020, DHL Global Express led the market with 6% of annual revenue. Kuehne+Nagel and DSV+GIL followed with 4% each, succeeded by DB Schlenker and Nippon Express at 3%. Fully 60% of the market is composed of “others” – smaller providers that hold less than 2%, and perhaps not more than a few deciles…

…Chen’s primary mission is to guide Flexport into a phase of automation that lays the groundwork for its global trade platform ambitions. When I asked Chen which initiatives best showcased the company’s burgeoning abilities on this front, he shared two examples.

First, Flexport is devoting serious resources to digitizing global trade documents. Ingesting data from different languages and formats is the first step in building a library of “facts” around a shipment. Instead of “optical character recognition,” or OCR, Flexport relies on machine learning models developed with Scale AI. Whereas Flexport used to transform accurate data from documentation in two days or less, the recent partnership has helped the company do it in minutes while maintaining 95% accuracy. Chen noted that the reason accuracy sat short of 100% was not a technical issue but a result of human error at the outset. 

Second, Flexport is developing models to predict when a shipment will arrive to be unloaded at a given port. Getting seemingly simple things right can have meaningful downstream effects. Knowing when a ship is ready to be unloaded influences when a truck should arrive, for example. This, in turn, impacts how a warehouse might organize its space. Just as snafus in one part of the supply chain can lead to misery elsewhere, improvements can create secondary and tertiary efficiencies. According to Chen, Flexport is well-positioned to produce reliable models to this end thanks to its existing freight forwarding business, saying, “We believe we probably have the highest accuracy.”  

2. #028 – PM Lessons by Meituan Co-Founder – Pt 22: Demand and Supply – Tao

Understanding demand and supply is hard. Although you can only be in one of two situations: 1. demand outstrips supply or 2. supply outstrips demand, it’s hard to know at any point in time, which situation you’re in.

In our day-to-day work, these are the common problems that we’d encounter that have to do with demand and supply:

1. The team doesn’t proactively determine the situation of demand and supply. As a result, the operation lacks focus, and the approach is basically “throw it against the wall and see what sticks“.
2. The team knows that demand and supply each affects the other, but can’t make a clear judgment about which one is more important.
3. The judgment about demand and supply is correct, but the operation is not guided by the prevailing demand and supply conditions.

If any of the above three situations occurs, then the team would usually work in the opposite direction of the demand-supply condition. In fact, they may even have a strong incentive to get it wrong!

For example, if a company is in a situation where supply outstrips demand, it should be doing more work on the demand side. In reality though, more often than not, it’s still pushing hard on the supply side.

Why?

If supply outstrips demand, then the demand is very important. The demand side buyers know they’re important, and they’d be very hard to entertain. In comparison, the supply side sellers would be a breeze to spend time with because they’re the ones who are in a hurry and have something to ask for. As such, the team would have a strong incentive to continue working on the supply side and pretend the harder-to-deal-with side is less important. This was a frequent occurrence in Meituan…

…Here’s another example from the retail industry to demonstrate how frequently people get the demand and supply relationship wrong.

I asked the bosses of convenience store and supermarket chains, “In the retail industry, which one is more important, demand or supply?“

Without exception, all of them answered that supply is more important.

It doesn’t gel with my business common sense – with the industrialized manufacturing that we have today, most goods should have more supply than there is demand.

Therefore, the next question to figure out is – is this how they think, or is this how they act?

So I asked them another question, “At your company, what work do you absolutely have to do yourself?“

They all answered, “Choosing the location“. One of them even said he personally chose the location for close to a thousand stores they have all over the country.

For retail, location is the aggregation of demand. They say supply is more important, but their actions are pretty revealing.

3. Arena Show Part I: Idea Dinner + YC Continuity – Benjamin Gilbert, David Rosenthal, Packy McCormick, Mario Gabriele, Shu Nyatta, and Anu Hariharan

Anu: It is so true. I also think it’s really hard to understand and appreciate an organization like YC from the outside. You really deeply understand YC in only two ways, if you’re a YC founder and if you work within YC.

When I was at Andreessen Horowitz, I actually did not understand the depth and the cultural nuance with which YC was built. It’s really hard to grasp that.

David: Can we talk about that for a minute? I put this in the notes. My current mental model of YC is like a university, a top Ivy League University. It’s very hard to get into. You take classes every year or every six months. There’s an endowment attached to it, which is Continuity now.

Ben: Wait, David. What do you mean by endowment? Are you saying that all of the proceeds from YC exits go into a big pool of capital that then funds Continuity? Is that what you’re suggesting by endowment?

David: No, but I’m curious if that’s the case. I meant more just like, it’s really weird that a large part of the private capital markets and the venture capital markets in America, those dollars come from educational institutions, mostly private educational institutions. That’s just very bizarre. Anyway, that’s kind of what I meant. Is that a good mental model of YC? What is it like?

Anu: Yes. In fact, we say that. We say YC is university for startups. Think of the accelerator as the undergraduate program and Continuity is the graduate school.

We are modeled after university in the sense of we have applications you don’t need to know anyone to apply to YC. Second, we were the first to do mass production of investments in a batch of startups. No one had ever done that. Everyone usually does, I met a set of companies, we have a Monday partner meeting, and you pick one or two.

YC from day one was a batch. They always received investments together. That, I think, goes to the insight that the founders of YC had at the time, which was entrepreneurship is lonely. Being in a group is how you motivate each other to learn from each other. And that’s your peer group.

Fundamentally, it came from the approach of a university. Continuity is graduate school. As I talked about, Series A is just one of the programs we run. We have two others, Post-A and Growth. Post-A focuses on two months within you raise the Series A. That’s a six-week program. We rebatch you, so now you have a new set of peers.

Our scale founders come teach how to form a recruiting team, how to hire engineers, because your job changes as a CEO. No one is writing a book about how your job changes and how to learn. Remember, the median age of a YC founder is 27, which means they have probably managed the sum total of three people in their life before these founders.

David: They really are like undergrads.

Anu: Yeah. You cannot expect them to know. How are you going to provide resources so that they can learn from others and they do as few mistakes as possible and as quickly as possible? Because when you’re scaling, you just go on a rocket ship, but the amount you demand out of these founders is a lot. The bar you’re setting is really high.

In our community, that’s why Brian Chesky comes to speak every batch. He’s the opening speaker of every batch. Right now, for all these programs that we run, the group program is how to scale as a CEO. That’s literally the program. It’s an eight-week session. It talks about hiring execs, performance, management, culture, and so on.

We have scaled founders and scaled exec. Tony Xu comes for that. His execs, the CFO of DoorDash, the head of engineering of DoorDash, come for the respective session. It’s really good to see the entire community working to transfer their learnings to the next batch of companies…

…Anu: At YC, I would say, if I had to pick one thing YC is really good at across both early and Continuity is we go by based on founders. I know it sounds cliche, but I think we also have an incredible advantage in assessing what makes a founder a really good founder.

We have incredible amounts of data, pattern recognition, and learning that we have honed it to a point that we know how to spot them. You all have heard of the famous 10-minute YC interview and everyone asks, how do you know in 10 minutes? The fact is we probably know in the first two minutes.

We actually don’t need the full 10 minutes. But sometimes, one or two people will surprise us with the end of the interview. I think the three things that can articulate what it is on the founder we look for.

One is the continuity stitch. Often in the growth stage, people pay attention to the founder, but they don’t. If you’re at a venture fund or a growth fund, you probably hang out with the founder for a week or two weeks before investment, some a total of three hours. By the time Continuity invest, I probably know them for years, or months, and I’ve had those interactions.

Ben: You’re saying that you’re paying attention more to the qualitative founder properties—even at the growth stage—than you are to their specific growth rate, or what their margins look like, or anything like that?

Anu: Yes, but if the three qualities hold, the metrics will show. I can either look at metrics, but sometimes metrics don’t tell you how good the internal sausage making is. Many people can package the metrics in a fundraise deck. It’s very well done. We teach you to do it.

We’re really experts at it. Therefore, we know it’s going to look great. We also teach them what points to emphasize on. We actually do practice runs. In Demo Day, we actually even write the script sometimes if they don’t understand what it is.

David: That’s a how-can-I-help moment.

Anu: Yeah. What we look for is, how fast does the founder move? What is how fast do they move mean? How fast do they ship? How fast do they iterate? Is it single biggest indicator and correlation to how successful they’re going to be and how soon?

You won’t be right about members’ many decisions early on, but at least, are you learning from them fast? And are you making changes? That’s one we measure. Second of the growth stage is how well are you hiring. If you’re sloppy in hiring, it always hits a wall.

One of the things we look for is how well are they hiring engineers, how well are they hiring execs. Will they be able to convince an incredible exec to come join them? That’s second. Third is clarity of thought. Clarity of thought in the growth stage for us is, can they write out two pages what makes this a $5 billion or a $10 billion company really well?

If you’re doing those three things, you’re going to be on top of your metrics, your product-market fit, your attention. There will be rough edges. I think because of YC, we’ve had the benefit of watching everyone from day one.

We know how Tony scaled. We know deeply well how Josh had Gusto scale. We know a lot of those founders. We then know, okay, these were rough edges, these are okay. These other founders had and this is how you and I know.

David: We’ve told a lot of these stories on Acquired. If you’re a growth investor looking at these companies new, you’re like, I know this is all going great, but you know those companies don’t always all go great. Tony had some serious near death moments. Airbnb was not up into the right journey the whole time.

Ben: If I had to summarize, I know we’re interviewing you. Not me here, but it seems like you invest based on the inputs rather than the outputs or maybe the leading indicators rather than the trailing indicators, where if somebody’s operating with those three principles, the business probably won’t consistently produce the results that someone would like to look for in the growth stage investment. They have a much higher probability at any given time of producing high quality results because those are the inputs that matter.

Anu: Absolutely. That’s why we feel strongly that inputs can be influenced. If you’re learning best practices and those are your inputs, then you can actually influence company building. When Tony comes and teaches our Growth Program and says these were my darkest moments, these are my mistakes I made, and I sure hope you don’t make the same mistakes, but these are two things I did really well, that’s incredibly valuable. That color is very hard to get outside of YC.

4. 2022 SaaS Crash – Alex Clayton

The rapid decline in value of public SaaS companies over the past 6 months has undoubtedly already had a huge impact on private market valuations. That downward trajectory may continue even if the public markets stay flat at today’s levels. If public market returns cannot fuel venture capital fundraising from their limited partners, the flywheel will slow down. Investors will have fewer dollars to invest, companies will have less cash to hire and invest in growth, and outcomes are likely to be much smaller than previously thought. This reset has been swift and will soon be painful for many businesses that are burning too much money and/or those that will have to slow top-line growth. Moreover, there will be wide-ranging implications for employees and investors not only in the SaaS community but for all private technology markets.

And while much of the focus has been on the decline in valuations, there is another huge factor that can’t be overlooked – how could a recession or broader economic slowdown affect your financial profile? This could have an even bigger impact on valuations if the fundamentals of businesses change for the worse. While a large part of the sell-off has consisted of a move away from riskier asset classes in sectors such as high-growth SaaS to cash and value stocks, recent earnings results have been strong and business fundamentals have not changed broadly. But what if you traded at 50x forward revenue and are now trading at 10x, and your associated forward revenue also dips by 30-40-50% from your prior plan? The outcome is not pretty and one we have not yet seen, but could soon if the 2008-2009 Great Recession is any indicator.

The following charts look at Salesforce* and NetSuite*, two publicly traded SaaS companies during the 2008 Great Recession, and what happened to their respective value and financial profiles. Unfortunately, while this is a small sample size, these are the best precedents as almost all other SaaS companies went public after the Great Recession…

…Salesforce was almost a $1B implied ARR (annualized revenue run-rate) business growing over 50% year-over-year at the start of 2008. During the Great Recession, revenue growth slowed to 20%. Non-GAAP operating margins did hold fairly steady, though…

…NetSuite was over $160M in implied ARR growing ~45% YoY at the end of 2008 before slowing dramatically. The company did not grow for 3 quarters in a row before accelerating back to growth. Similar to Salesforce, they also held non-GAAP operating margins constant but slowed investment significantly. It would be hard to imagine a ~$150M ARR business today that’s growing fast grinding to a halt, but this happened for NetSuite. The company also sold to SMBs and the mid-market, a segment that was hit particularly hard during the Great Recession.

5. TIP447: How To Build A Human Bias Defense System w/ Gary Mishuris – Trey Lockerbie and Gary Mishuris

Trey Lockerbie (16:25):

Fascinating stuff. So I want to move on to the next one, which is base-rate neglect. So there’s this phrase that’s come up, I don’t know, maybe over the last decade, maybe longer, but it’s don’t fight the Fed. And we’ve seen a lot of help from the Fed when markets have declined in the past and we’ve seen the Fed reverse course on say, raising interest rates quickly due to recessions and other liquidation problems around the world. So from this, we may have misconceived notions on how either the Fed will react to markets if they continue to decline from here, for example, which would thus enact this base-rate neglect human bias. So walk us through what the base-rate neglect bias is and how we might be able to avoid it.

Gary Mishuris (17:05):

Yeah. So I think it’s fascinating that… And I think sometimes people talk about inside view versus outside view. So base-rate neglect refers to ignoring the experience of others in similar situations and just making an assumption based on what we think we can do in this situation. So let’s say a very simplistic example of someone flips coins 1,000 times, they get 50% heads, 50% tails for a fair coin. And somehow we convince ourself that we can take a fair coin and flip tails 70% of the time. And that sounds ridiculous when they phrase it that way, but sometimes essentially that’s what is happening.

Gary Mishuris (17:42):

So, for example, if you study great investment records, which I’m sure you do, you realize that there’s a certain range of access returns over decades that the best investors have been capable of. And if you take Warren Buffett out of the picture and if you take people who use leverage out of the picture, unlevered returns, there’s almost nobody over decades has exceeded 5% per year access returns with no leverage and so forth. Obviously, Buffett has done close to 10, but I don’t think there’s going to be another Buffett necessarily.

Gary Mishuris (18:11):

So when someone shows up and they think they can do 10, what they’re doing is they’re exhibiting example of base-rate neglect. They’re looking at their own strategy and they’re saying, I have these clever mental models, I have this process, I have this special sauce. So they start to believe their own marketing deck a little bit too much, and they forget that the people who tried and failed to achieve the 10% for years, as an example, have also had their special sauce and their analyst teams and this and that, and yet they were only able to do a certain…

Gary Mishuris (18:42):

Think about someone like John Neff who record is public or who had three decades of returns. He beat the market by 3% per year in arguably less efficient markets than they are today. So when someone shows up and says, “Oh, I’m going to beat the market by 10%,” that’s a little bit crazy, it’s a little bit arrogant. And again, I think we’re all overconfident, but come back to the Fed. So look at the last 10 years, we had almost a perfect confluence of events. We had interest rates coming down. We had unrivaled Fed manipulation of markets far beyond just the short term end of the curve. We had maybe as a result there or maybe as a coincidence, huge amount of speculation, both by retail investors and by a number of “institutional” investors, institutional in quotes, not naming any names, don’t ask. And you basically had over the last five years, you had 25% CAGR for large growth stocks or all cap growth stocks.

Gary Mishuris (19:33):

So if you are investing in the universe, it’s pretty easy to start believing your own BS and start saying, well, gee, yeah, no, I can crush the… I can do 20, 25% per year, but like really? Let’s zoom out over the long term US equities return inflation plus six to seven, depending on the time period. So if you think you can do 20% plus, you think you’re going to beat the market by double digit percent per year. And I know everyone thinks they’re very special, but that’s just a perfect example of the inside view. The inside view is all these specific details for why the past experience of others doesn’t matter. And the base rate is the past experience of others in a similar situation. And I think the best thing you can do is zoom out and say, “Well, whatever I think about my own capabilities, let me put a heavy weight on the experience of others and a small weight on why I think I’m going to do so much better.” And that’s probably the best you can do.

Trey Lockerbie (20:25):

That’s interesting, because I was wondering the distinction here between say the base-rate neglect effect versus say the recency bias effect, because what I was describing, I don’t know, it could maybe fall into both categories depending on how you look at it. So recency bias is when you’re essentially taking events from the past and extrapolating them into the future. So how exactly is that different or what are maybe some other distinctions between that and the base-rate neglect effect?

Gary Mishuris (20:49):

So I think a recency bias is almost a special case of base-rate neglect. So what are some examples of recency bias? Let’s say you have a company over the last couple of years, it’s been growing 30% per year and you assume it’s going to grow at 30% per year for the next one year. I’m obviously using extreme example. So that’s recency bias. You take a near term past and assume that’s going to be the same in the long term future. On the other side, let’s say you have a company that over the cycle has barely earned its cost of capital and averaged a dollar per share. But now the last couple of years been earning $2 per share and averaging 20% return on capital. So you are going to extrapolate that $2 and assume that’s the new normalized earnings for the business and say the new long term average earnings is $2. And this now all of a sudden, the 20% return on capital business or something like that.

Gary Mishuris (21:40):

In each case, you’re ignoring the base rate, the base rate in this case being the history of the company or the history of similar companies. So in the first case, the history of companies growing 30% for two years is mean version in the growth rate towards the growth rate in all companies. So just to level set everything that the average company’s profits over long periods of time grow in line with nominal GDP. But, by the way, ironically, if you look at Wall Street estimates, hey, now they assume the average company grow is going to grow earnings in double digits. Well, it hasn’t, it’s been growing five to 6%. And that’s an example of base-rate neglect because they forget that a fifth of the market is going to have negative earnings growth, but that’s a separate thing.

Gary Mishuris (22:20):

And then the base rate for a company that’s been earning its cost of capital and had a couple of good years is that the long term history is much more likely to be the best predictor than the last couple of years, which could be a cyclical high or something like that. So I think ignoring the base rate leads to the recency bias, where we put a disproportionate weight on what just happened and assume that’s a proxy for what’s going to happen as opposed to zooming out and looking at a much longer data series…

…Gary Mishuris (54:36):

And like you said, if you have areas where you don’t invest, that squeezes those 10 to 15 investments into the rest of the opportunity set, meaning that you might be correlated. But it’s not about gig sectors, which is a common misconception. So I’ll give you an example. So prior to starting Silver Ring, I managed a fund at my prior employer and I had two investments. One was SABMiller, which was a beer company, and the second one was Qualcomm. If you are running some bar risk model, and you’re looking at overlap, they’re completely different gig sectors. One is technology, the other is consumer. So no relationship, you’re good, you’re diversified. But the thesis for each one was predicated on rising middle class in emerging markets, meaning people were going to trade up and buy more expensive beer in China and other emerging economies and people were going to trade up to fancier smartphones, which was going to drive demand for Qualcomm’s products.

Gary Mishuris (55:28):

So here are two completely different industries where the same macro force, which is a tailwind, if it doesn’t play out would hurt the thesis. So looking for those correlations as systematically as possible, and thinking about what do I have to be right about each business five plus years out as opposed to what do I have to be right about each stock five quarters out, that’s the mindset you want to have. And also frankly, you have a set of risk reward trade-offs. Too many people make the mistake of sizing their largest investments based on upside. But again, going back to the safety first mentality, I size my positions based on downside, meaning my largest investments have the smallest downside. I have an investment, which maybe it’s a 30% of my base case value as to 30 cents in a dollar, but if that has 100% downside, that might not be my biggest position. So again, you want to have multiple layers of defense.

6. The Transcript Q1 2022 Letter – Scott Krisiloff and Erick Mokaya

Investors are asking whether this is the end of an era. For nearly 15 years global policymakers have battled a deflationary mindset with near-zero interest rates and quantitative easing. However, a series of supply chain shocks and monetary policy errors have sparked rising long-term inflation expectations. If we have exited the deflation era and entered into an inflationary one, it will mean structural changes in monetary policy, interest rates, and stock multiples. By the Fed’s own account, despite raising interest rates by 0.75% so far this year, it is still only on pace to get to a neutral interest rate by the end of the year. It has not yet entered the restrictive territory, which would usually be justified by >8% inflation.

“We’ve been accustomed to 40 years, basically, of one cycle, the whole cycle that we covered in the last quarterly review. Declining interest rates, declining tax rates, all these trends – – it’s all come to an end. Not just an end, it’s actually changing. But people haven’t wrapped their heads around that yet…There’s going to be a new cycle.” – Horizon Kinetics (INFL) Co-Founder Steven Bregman

“An entire generation of entrepreneurs & tech investors built their entire perspectives on valuation during the second half of a 13-year amazing bull market run. The “unlearning” process could be painful, surprising, & unsettling to many. I anticipate denial.” – Benchmark Capital General Partner Bill Gurley

While the Fed has talked about getting to “neutral” throughout this quarter, it hasn’t yet set an expectation for what neutral means. There seems to be some consensus that this neutral rate would be a short-term interest rate in the 2.5% – 3.5% range. Equity markets may not yet reflect this new cost of capital.

“I think I’m in the same areas as my colleagues philosophically. I think it’s really important that we get to neutral and do that in an expeditious way. “ – Atlanta Fed President Raphael Bostic

“I like to think of it as expeditiously marching towards neutral. It’s clear the economy doesn’t need the accommodation we’re providing. And so in order not to tip the economy over by reacting abruptly, we need to take a measured pace. But that measured pace still gets us up to the neutral rate, which I put at about 2.5% by the end of the year.” – San Francisco President Mary Daly…

…The Transcript is also closely watching continued lockdowns in China. The Chinese government’s zero-Covid policy has left hundreds of millions of people in lockdown even though the rest of the world has returned to normal. The effects of this supply chain shock have still not entirely made their way into the economic discussion.

“I think a separate risk is kind of the impact of logistics and supply chains as we deliver product to China and from a more macro perspective just the port closures and the broader impact that we could see in China given the degree of exports they have just generally across the economy. With respect to the China quota difficult to predict.” – Intuitive Surgical (ISRG) CFO Jamie Samath

“...the situation in China is unprecedented. Shanghai, a city 4x the size of New York City, is completely locked down…China continues to battle COVID resurgences and navigate through prolonged lockdowns.” – Starbucks (SBUX) CEO Howard Schultz…

…Surprisingly, consumer spending has still only been moderately affected by surging inflation and falling financial markets. The covid-era stimulus has left consumer bank accounts with lots of reserves and consumers still have a significant amount of pent-up demand for travel, restaurants, and other entertainment. We are expecting to see some slowing of consumer spending and the real economy going forward in sympathy with the dynamics of capital markets.

“March was the eighth straight month in which inflation outpaced income with lower-income consumers being most impacted by rising energy and food prices.” – Wells Fargo (WFC) CEO Charlie Scharf

“Consumers are trying to ration their money a little bit more carefully because they’re trying to smooth out their cash flow.” – Affirm (AFRM) CEO Max Levchin

“when we think about where inflation is, there’s absolutely pressure on that low and middle income consumer.” – Macy’s (M) CEO Adrian V. Mitchell…

…No one really knows whether this is truly the end of an era and the start of an inflationary epoch, but this period is not without historical analogue. The transition from the deflationary 1930s to the inflationary 1940s was caused by World War II, which was also a time of intense supply chain disruptions coupled with huge economic stimulus. The Covid period has some similarities. Immediately following the war there was sharp and severe inflation for several years, which was ultimately brought under control by changes in monetary policy. In the longer term, huge investment in industrial capacity and human capital led to a consumer renaissance and low inflation in the 1950s.

The inflationary period of 1966-1980 is also worth studying if we are entering a new inflationary phase. An important takeaway from that period is that a surge in interest rates does not necessarily happen overnight. Instead, it happened in fits and starts over the course of several bear markets and recessions. Stock multiples ended that period in single digits, but the nominal value of the Dow hovered around 1,000 for more than a decade.

We may be entering a period in which the Fed raises interest rates more frequently than it lowers them, but the Fed is still very reluctant to cause a recession. If it looks like higher interest rates are putting employment at risk, the Fed is likely to abruptly change course despite inflation. The result would probably be positive for capital market valuations.

“You can’t think of a worse environment than where we are right now for financial assets..I think we’re in one of those very difficult periods where simply capital preservation is I think the most important thing we can strive for. I don’t know if it’s going to be one of those periods where you’re actually trying to make money.” – Tudor Investment Corporation Co-Founder Paul Tudor Jones

7. Trying Too Hard – Morgan Housel

Thomas McCrae was a young 19th Century doctor still unsure of his skills. One day he diagnosed a patient with a common, insignificant stomach ailment. McCrae’s medical school professor watched the diagnosis and interrupted with every student’s nightmare: In fact, the patient had a rare and serious disease. McCrae had never heard of it.

The diagnosis required immediate surgery. After opening the patient up, the professor realized that McCrae’s initial diagnosis was correct. The patient was fine.

McCrae later wrote that he actually felt fortunate for having never heard of the rare disease.

It allowed his mind settle on the most likely diagnosis, rather than be burdened by searching for rare diseases, like his more-educated professor. He wrote: “The moral of this is not that ignorance is an advantage. But some of us are too much attracted by the thought of rare things and forget the law of averages in diagnosis.”

A truth that applies to almost every field is that it’s possible to try too hard, and when doing so you can get worse results than those who knew less, cared less, and put in less effort than you did…

…But there are mistakes that only an expert can make. Errors – often catastrophic – that novices aren’t smart enough to make because they lack the information and experience needed to try to exploit an opportunity that doesn’t exist…

…Marc Andreessen explained how this has worked in tech: “All of the ideas that people had in the 1990s were basically all correct. They were just early.” The infrastructure necessary to make most tech businesses work didn’t exist in the 1990s. But it does exist today. So almost every business plan that was mocked for being a ridiculous idea that failed is now, 20 years later, a viable industry. Pets.com was ridiculed – how could that ever work? – but Chewy is now worth more than $10 billion.

Experiencing what didn’t work in 1995 may have left you incapable of realizing what could work in 2015. The experts of one era were disadvantaged over the new crop of thinkers who weren’t burdened with old wisdom…

…Doctors have their own version, as one article highlights:

“Almost all medical professionals have seen what we call “futile care” being performed on people. That’s when doctors bring the cutting edge of technology to bear on a grievously ill person near the end of life. The patient will get cut open, perforated with tubes, hooked up to machines, and assaulted with drugs. All of this occurs in the Intensive Care Unit at a cost of tens of thousands of dollars a day.

What it buys is misery we would not inflict on a terrorist. I cannot count the number of times fellow physicians have told me, in words that vary only slightly, “Promise me if you find me like this that you’ll kill me.” They mean it. Some medical personnel wear medallions stamped “NO CODE” to tell physicians not to perform CPR on them. I have even seen it as a tattoo.

The trouble is that even doctors who hate to administer futile care must find a way to address the wishes of patients and families. Imagine, once again, the emergency room with those grieving, possibly hysterical, family members. They do not know the doctor. Establishing trust and confidence under such circumstances is a very delicate thing. People are prepared to think the doctor is acting out of base motives, trying to save time, or money, or effort, especially if the doctor is advising against further treatment.”

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Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. Of all the companies mentioned, we currently have a vested interest in Intuitive Surgical, Meituan, Salesforce, and Starbucks. Holdings are subject to change at any time.

We’ve constantly been sharing a list of our recent reads in our weekly emails for The Good Investors.

Do subscribe for our weekly updates through the orange box in the blog (it’s on the side if you’re using a computer, and all the way at the bottom if you’re using mobile) – it’s free!

But since our readership-audience for The Good Investors is wider than our subscriber base, we think sharing the reading list regularly on the blog itself can benefit even more people. The articles we share touch on a wide range of topics, including investing, business, and the world in general.

Here are the articles for the week ending 15 May 2022:

1. An Interview with Coda Founder (and Bundle Expert) Shishir Mehrotra – Ben Thompson and Shishir Mehrotra

SM: Maybe we can start with some background on where I came from. Before I started Coda, I spent about six years running the YouTube products at Google. Most of that time, our presumption was that YouTube was going to be an ad-supported product. Obviously, this is still how the majority of YouTube is run, but we always thought that at some point we would add in this paid model, and we’d have some way for creators to make money from payments or subscriptions, or so on, but it wasn’t ever a top priority.

We tried things, but we always put our fifth or six priority on it, and they never really worked, I think over time I counted ten different experiments that we tried. One of my favorite ones was we spent nine months on this paid platform launch, it made a hundred dollars. Not a hundred dollars per day, or a hundred dollars per week, but all time, a hundred dollars. We bought pizza for the team, we shut it down.

Through this period, we keep trying these experiments, none of them work. At some point, I ended up having this conversation with a friend at actually one of the cable companies. We’re describing how this was working and he asked me how we felt about bundling. I said, “Oh yeah, we’ve tried everything, but we’re not going to do bundling. Bundling is evil.” He said, “What do you mean bundling is evil?” It was very interesting, it just stuck in my head.

This part, I definitely disagree with you, because I think bundling is the most amazing thing ever.

SM: For sure! That’s where I changed my mind. But it’s like, you ask anybody about bundling and first off, we say the word bundling, what do they think of? In the US, they think of Comcast and nobody really has positive interpretations of Comcast. In their head, they’re thinking bundling is cheating somebody, but we basically came to the conclusion that that is incorrect. So I developed, started doing all this research, and started coming up with this framework of what became this paper Four Myths of Bundling.

The core idea is that bundling is actually beneficial to all three parties. It’s beneficial to the consumer, to the providers, and to the bundler. This is because the heart of bundling is based off of balancing the needs of superfans and casual fans, I think this is one of the things that people often mistake about it. Of the four myths, the fourth one is the one that’s most cited, which is the reality that the best bundles are the ones that minimize superfan overlap and maximize casual fan overlap…

…I think it’s always been so important to have something physical — in the case of cable companies, they had a wire. That was an obvious bundling point that actually had nothing to do with the programming. You got the widest possible array of stuff that at the same time made total sense together.

SM: Yeah. When we talk about this at Spotify, we call this go-to-market alignment instead of superfan alignment, people mistake the two. A famous Spotify bundle that really worked was the student bundle. If you’re a student, you can get Hulu, Spotify, and Showtime for five bucks a month. Most people think that’s a big loss leader, or a marketing stunt, or so on — it’s not at all. It makes tons of money for all three parties, and has grown that business a lot. But one of the things that makes it work is that the wire, to use your analogy, is the student. What are all the things the student needs when they go to school? You can start stacking all these services into it. To pick something out of the B2B world, the most famous bundler in that world is Microsoft…

...As I understand it, it was kind of how you got connected with Daniel Ek and you ended up joining the Spotify board. I’m curious, to the extent you can talk about it, beyond that student bundle, how does your thinking about bundling impact the way Spotify is approaching things? Is podcasting a bundle play? How expansive is this?

SM: The student thing was probably the first step, but by far the biggest bundling experiment at Spotify is podcasting. I think the core idea — Daniel and I started riffing on this, to give lot of the credit, Daniel had basically the same ideas, we were very aligned on how to think about this. He just asked me to help formalize them and write them down, which turned into the Four Mythos of Bundling doc.

At that time, Spotify was synonymous with music and still is for a lot of people. One of the insights that Daniel had, in thinking about bundling, is what if we were to take something, let’s use your wire analogy, that still had a through line that people could understand, but drew a totally different group of superfans.

Daniel uses this line a lot that the video market, depending on how you size it is call it a trillion dollars in revenue a year — the audio market, radio and so on, is a tenth of that. He often talks about this idea that “Are your ears really worth the 10th of your eyes”? Of course not, it doesn’t make any sense, and he talks about how that market can grow. But all of that was I think Daniel did this genius job of saying, “Hey, this through line is going to be audio,” but fundamentally, what we’re going to do is we’re going to take products for which the superfans have incredibly distinct audiences. The set of people that care about listening to mystery podcasts, or to news bloggers, or to sports bloggers, or so on.

Or subscriptions to podcasts like Stratechery, available on Spotify.

SM: Exactly! We should talk more about that too; I think the fact that’s available on Spotify is amazing. But I think that idea of, “We’re going to pull this thing together”, is absolutely the idea of minimizing superfan overlap, maximizing casual fan overlap. You can actually see it, we have a team of economists at Spotify that try to measure that impact, there’s a part of the paper that talks about this concept we call Marginal Churn Contribution (MCC), which is, if you think about how you should divide up accountability or money in a bundle, we believe the right way to do it is something we call marginal churn contribution, which is if you were to remove this thing, how many people would churn from the product?

I love that. I’m going to completely steal that terminology, because you see this again and again when people are talking about sports, “Why does ESPN command so much money?”, or “Why do regional sports networks command so much money relative to their tiny audience sizes?” It’s this exact point. If you really like sports and your bundle does not have ESPN, you are going to leave. We saw this last year: Disney just put YouTube TV over the barrel because they tried to go one day without ESPN. It was like, “Nope, not going back going to happen.”

SM: Right. If you’re a Lakers fan, you’re going to end up getting that network. It works. The mistake many people make is they try to correlate usage with MCC, with marginal return contribution, and it’s generally wrong. In fact, if you were to take a graph and you plot on one axis you plot usage, on the other axis, you plot MCC, sometimes people call it anchor value, you could draw a diagonal line through it and everything below that line is things where usage matters more than anchor value, or more than MCC and above that line is things where your MCC matters more than usage. You get two completely different business models. For example, much of the content we had on YouTube at the time drove significantly more usage than it did MCC, if I removed any piece of it, you would still just come.

Which is sort of the UGC [user-generated content] idea in general, isn’t it? In that case, you’ve completely commoditized your suppliers because there is no special supply. There’s always other supply to put there.

SM: Well that’s not a very positive way to think about it! What it does do is it leads to an advertising-based business model. If usage is more important than MCC, then the right way to monetize that product is probably advertising. On the other hand, if you’re above that diagonal, and you have things where access is more important than usage, no matter how much I love your podcast and your newsletter, I can’t listen to them over and over again, I’m not going to read the newsletter over and over again.

Please don’t.

SM: You’re only going to get so much usage out of me, but I pay for it because why? I’m sure you’ve asked, but if you ask people, “Why do you pay for Stratechery”? I think it makes me feel smarter, I feel better informed when I’m in this other discussion. I think you know that you’re well read by some really important people out there. It creates a common understanding for us, but it’s uncorrelated to how much time they spend on it, it’d be a dumb way to measure it.

I get in trouble when I go too long.

SM: Right! Exactly. I don’t want that, I want synthesis out of you. One of the things that happens, one of the reasons I’m so excited about the bundling work, it’s a fun theory. People have all sorts of different hobbies, I have this weird one, I like bundling. (laughing) I have rather normal hobbies too!

(laughing) Theoretically, if this weren’t a podcast to talk about Coda, I’d be like, “Oh my God, I can talk about bundling for an hour. I’m ready to go.”

SM: That’s right. Well, I’ll tell you why the concept of bundling is relevant. We talk about it with some very literal examples, and you talk about product bundling, and Comcast, and so on. But the core idea of “people value access over usage” is a really interesting idea. This idea of marginal churn contribution actually applies to products in general. You’re building a product, and you like Coda, and you say, “Hey, what should I do next?” You kind of have two choices. You have things that are going to drive usage and things that are going to drive new users, you’re going to create MCC. You can apply the exact same philosophy the same way, “I’m going to add this thing, I think it’s going to add new users”, or prevent them from churning, versus things that are going to increase usage. When you use that framework, you see the world a little bit differently, you think about marginal impact, which is much more powerful than some of the other way of measuring success.

2. Terra Flops – Matt Levine

An “algorithmic stablecoin” sounds complicated, and there are a lot of people with incentives to pretend that it is complicated, but it is not. Here is how an algorithmic stablecoin works[1]:

1. You wake up one morning and invent two crypto tokens.

2. One of them is the stablecoin, which I will call “Terra,” for reasons that will become apparent.

3. The other one is not the stablecoin. I will call it “Luna.”

4. To be clear, they are both just things you made up, just numbers on a ledger. (Probably the ledger is maintained on a decentralized blockchain, though in theory you could do this on your computer in Excel.)

5. You try to find people to buy them.

6. Luna will trade at some price determined by supply and demand. If you make it up on your computer and keep the list in Excel and smirk when you tell people about this, that price will be zero, and none of this will work.

7. But if you do a good job of marketing Luna, that price will not be zero. If the price is not zero then you’re in business.

8. You promise that people can always exchange one Terra for $1 worth of Luna. If Luna trades at $0.10, then one Terra will get you 10 Luna. If Luna trades at $20, then one Terra will get you 0.05 Luna. Doesn’t matter. The price of Luna is arbitrary, but one Terra always gets you $1 worth of Luna. (And vice versa: People can always exchange $1 worth of Luna for one Terra.)

9. You set up an automated smart contract — the “algorithm” in “algorithmic stablecoin” — to let people exchange their Terras for Lunas and Lunas for Terras.[2]

10. Terra should trade at $1. If it trades above $1, people — arbitrageurs — can buy $1 worth of Luna for $1 and exchange them for one Terra worth more than a dollar, for an instant profit. If it trades below $1, people can buy one Terra for less than a dollar and exchange it for $1 worth of Luna, for an instant profit. These arbitrage trades push the price of Terra back to $1 if it ever goes higher or lower.

11. The price of Luna will fluctuate. Over time, as trust in this ecosystem grows, it will probably mostly go up. But that is not essential to the stablecoin concept. As long as Luna robustly has a non-zero value, you can exchange one Terra for some quantity of Luna that is worth $1, which means Terra should be worth $1, which means that its value should be stable

All of this is, I think, quite straightforward and correct, except for Point 7, which is insane. If you overcome that — if you can find a way to make Luna worth some nonzero amount of money — then everything works fine. That is the whole ballgame. In theory this seems hard, since you just made up Luna. In practice it seems very easy, as there are dozens and dozens of cryptocurrencies that someone just made up that are now worth billions of dollars. The principal ways to do this are:

• Collect some transaction fees from people who exchange Luna for Terra or Terra for Luna, and then pay some of those fees to holders of Luna as, effectively, interest on their Luna holdings. (Or pay interest on Terra, creating demand for Luna that people can exchange into Terra to get the interest.[3])
• Talk about building an ecosystem of smart contracts, programmable money, etc. on top of Terra and Luna, so that people treat Luna as a way to use that ecosystem — as effectively stock in the company that you are building and ascribe a lot of value to it.

These things reinforce each other: The more fees you collect and distribute to Luna holders, the more big and viable your ecosystem looks, so the more highly people value it, so the more Luna they buy, so the more activity you have, so the more fees you collect, etc.

But there is no magic here. There is no algorithm to guarantee that Luna is always worth some amount of money. The algorithm just lets people exchange Terra for Luna. Luna is valuable if people think it’s valuable and believe in the long-term value of the system that you are building, and not if they don’t.

The danger here is that Point 7 never goes away. Any morning, people could wake up and say “wait a minute, you just made up this all up, it’s worthless,” and decide to dump their Lunas and Terras. If people decide to dump their Lunas then the price of Luna goes down.

If people decide to dump their Terras — “wait,” you say, “there’s an algorithm; the price of Terra can’t go down.” If people decide to dump their Terras, then the price of Terra goes down from $1 to like $0.97, and arbitrageurs step in, buy Terras for $0.97 and exchange them for $1 worth of Luna.

Yeah. Well. The problem is that if people lose confidence in this system, they decide to dump both Lunas and Terras. Someone sells some Terras. Arbitrageurs step in, buy Terra for $0.99, and exchange it for $1 worth of Luna. Luna is at, say, $40, so each Terra gets you 0.025 Luna. Then the arbitrageurs sell their 0.025 Luna in the market, which drives down the price of Luna, which is falling anyway. Someone else sells some Terras, but now Luna is at $20, so each Terra gets 0.05 Luna, which arbitrageurs sell, and now Luna is at $10, so each Terra gets you 0.10 Luna, which then get sold, so Luna goes to $5, so each Terra gets you 0.2 Luna, etc. There is no natural stopping point for this process because Luna is just a thing you made up, and because it represents essentially confidence in your ecosystem, and as the price of Luna crashes that confidence ebbs away. And so eventually Luna trades at $0.0001 and you exchange one Terra for 10,000 Luna and you try to sell them and there are no buyers and so no one wants to arbitrage the price of Terra and so the price of Terra falls below $1 and everyone gives up on the stablecoin and the ecosystem and everything and it all goes to zero.

3. Jeff Jordan – Building & Investing in Marketplaces – Patrick O’Shaughnessy and Jeff Jordan

[00:05:31] Patrick: eBay, since it’s literally the perfect model of a marketplace is maybe the place to focus on for now. What kinds of actions did that mean when you were at eBay operating to try to promote price discovery or price equilibrium or something like that? What were you literally doing?

[00:05:46] Jeff: The most interesting thing is early on we try all these initiatives that we baked on our own and debuted the community. And we found out the leverage was way more to watch what the nascent behavior the community was doing and seek to amplify it. So the iconic thing there is Simon Rothman who’s bounced around. He’s a Valley veteran now. Early on in his career was just a early exec there and he has a very high interest in collectible cars. And one day I think he was searching for Maserati or Ferrari and expecting to see little replica cars and he found real ones. And it’s just like, “Why are people selling Lamborghini’s on eBay?” Well, it turned out Lamborghini’s are only sold on the coasts. And so if you’re in the middle of the country, it’s very hard to buy one typically and eBay entrepreneurs were figuring out, “Okay, here’s what we do.” So we took that nascent behavior and built eBay Motors, which then made it much easier to list and discover cars, generated the supply and created the awareness. The best actions we had was watch that nascent community behavior and amplify.

[00:06:51] Patrick: When you’re looking at a new marketplace for the first time, I’ll hold off on the discussion between horizontal and vertical marketplaces which we’ll come to at some point. But if you’re just looking de novo at a marketplace as an investor with your investor hat on, what are the features that you are zoomed in on most quickly that matter to you with all this experience?

[00:07:09] Jeff: Two main ones. One is fragmentation of the marketplace. I often have used the difference between OpenTable and Fandango in explaining this. OpenTable, the average restaurant owner on OpenTable owns one restaurant. And so aggregating them is a pain in the ass. But once you’ve aggregated them, it’s a very valuable thing. Whereas Fandango basically has deals with the five or six major theater chains and any one of them can have market power because if AMC pulls out of Fandango, I am motivated to go to amc.com and figure it out. When I was explaining this theory to a fellow board member and accolade Michael Klein and I explained the theory and he looks at me very quizzically and I go, “What?” He goes, “You do know I’m the founder of Fandango, right?” You’re like, “Oh crap.” So one is fragmentation.

The other is ideally lead gen. You’re creating relationships that otherwise wouldn’t have been created. The thing you try to avoid is “Okay, I have a relationship with my car repair man, my hair stylist, my whatever and it’s a frequent relationship.” Those don’t do well because the service provider, they’ll pay a little bit for convenience. They’ll pay a whole lot for a new customer. Ideally you have a combination of currently inefficient market that’s very fragmented and lead gen is a part of it. So Airbnb has lead gen. Hosts are being introduced to guests they never would’ve known. It’s spectacularly fragmented. The average host owns one property. It has those two characteristics.

[00:08:38] Patrick: Maybe we should just go read Andrew’s book to answer this question, but what have you seen in common amongst marketplace businesses that are especially good at thinking about that lead gen part of the equation? Because the fragmented supply side or the fragmented supplier base, like you said, it’s a pain in the ass to get them all, but it’s kind of straightforward, like you just got to go get them all. What about on that other side, what’s shared in common amongst the most talented people that you’ve seen thinking through this problem of lead gen?

[00:09:03] Jeff: The best models are ones that don’t really rely much on paid acquisition. The best entrepreneurs have figured out hacks to get user demand at scale through a user proposition. And one of the most brilliant hacks on this was the OpenTable hack that preceded me. The team figured it out ahead of time is they build a widget that restaurants could put on their own websites to empower online reservations, because the typical behavior at the time is “I want to go to The Slanted Door.” Okay, let me search on Google for the Slanted Door so I can find the telephone number. Go to the website and you see this widget that says make an online reservation. It’s just like, “Oh I’d rather do that than pick up the phone and have that experience of, ‘Can you hold sir?’ get back to you and then call multiple restaurants.” Just awful.

And so we put it on there and what it ended up doing, the diner would click on it and was redirected to The Slanted Door page on OpenTable. They would then discover, “Oh my God, I can make an online reservation at all these” and they’d come back to OpenTable. They wouldn’t go back to Google. They’d quickly learn a behavior to go do OpenTable. OpenTable was getting paid to acquire their restaurants consumers. While I was there, we didn’t spend a penny on demand acquisition and we’re growing very nicely based on that. So the best models don’t really rely on paid. They figured out some other way to get that distribution…

…[00:12:09] Patrick: Talent density. Obviously eBay is sort of like patient zero for this online digital marketplace concept. I’m sure working with Pierre there was a fascinating experience. You were there right in the thick of it. What stands out as the most important things that you learned as an operator at eBay?

[00:12:24] Jeff: I learned to be an operator. I’d only had a couple semi operating jobs up to that point. While I was a CFO at the Disney stores, I was also responsible for managing the Disney stores in Japan, but we had someone on the ground so I was kind of overseeing the person who was overseeing it. When I got to eBay, I’d never really run anything. And so I joined, Meg was building bench depth so she found a job for me and had me managing two people, one of whom promptly quit to go run a Baja Fresh franchise, which at that point might not have been the best financial decision unless he owns Baja Fresh at this point. I was managing one person, then a few months in she reorganized and gave me eBay North America, which was the ebay.com website. Seven years later, I was managing 5,000 people.

One of the blog posts that I get the most comments on is I think it’s titled Leaving It All On the Field. It brings a sports analogy to managing a hyper growth business. Because early on you’re the player, things are crashing around you and you’re making every call. And then there was a point where I remember one night when I go home, I get to work at 5:00 AM and it’s seven at night, there’s still a line outside my door waiting for me to make decisions. I go, “This is not scaling. I got to change something.”

And you become a coach. You hire a bunch of people. You try to get them into a place where they’d make most of decisions similar to how you would. And then the mode’s very different. You turn into a coach. At some point with hyper growth, they can’t make all the decisions. So they have to build a team. They become the coach and you become a general manager and you’re further and further from the action in the field each time.

And then take it to its logical conclusion, at PayPal with 5,000 people I was commissioner of the league. And it’s interesting, the job is fundamentally different. You’re not in the action. You are orchestrating it. I called it a bunch of -tions, organization, motivation, communication. And I didn’t like the job anywhere near as much. I was very gratified that I actually appeared to be pretty good at it. But my career was just, I continually went back to earlier stages. eBay grew, I went to PayPal, PayPal grew, I went to OpenTable, OpenTable grew… And there’s a point at which the good news is I got pretty good at that stage of growth, consumer marketplace businesses at that stage of growth.

The bad news is the learning curve just shallowed out like crazy. When I’m operating, I’m always on, always stressed, always tired. And then you throw on board on top of it and that was a pretty toxic combination…

…[00:22:08] Patrick: What were some of the early surprising aspects of coming at it from the investor side? I’m especially interested in the pricing of rounds. I was told to ask you about pricing Instacart, for example. What lessons did you learn on the investing side that were completely new and different in those early years?

[00:22:24] Jeff: The good news is I was looking for a steep learning curve and it was way steeper than I thought. I was like, “Wait, I’m in the same room. I’m just taking a different chair. How can it be that different?” And man, is it different. Lesson one was it is a steep learning curve. Some of the early lessons, and still learning them, which is the interesting part, 10 years in. You have to continue to be adopting your decision framework. One was whenever I saw a bargain, I should run. It’s a sign of no heat. Whenever I did a bargain, I regretted it later. Whenever I was forced to pay up, to date that has been a very good basket of companies. And you mentioned Instacart. I saw Instacart late when Apoorva was raising. I think he saw a blog I wrote on demand economy and just reached out. And he goes, “Listen, really late process but we’d would love to talk you.”

So we have this great conversation. And I think it was a Thursday or a Friday. And he goes, “Listen, I have to decide by the end of the weekend. I’m getting so much pressure.” I crunch away on the weekend, digging into the details. I want to do it. I get okay for my partners to go in with a number. And I think it was I go in with something that’s a 100x current GMV, like $90 million. And he goes, “Jeff, I’ve really enjoyed our conversations. I’d really love to work with you, but you’ve got to know you’re less than a third of any of my other term sheets. And by the way, I’m deciding tomorrow.” And so then do I want to play? If I want to play, I’ve got to triple. And so over a weekend… The interesting one, going back to your partners and saying, “You know I asked for $90? I need $300.” that was a gut check, but there was so much to like about it. They’re like, “Okay, I’m going to climb the ladder.” I’m glad I climbed the ladder on it. A lot of the very best deals have that kind of pricing pressure, and the pricing’s set by the market. It’s not set by metrics. So you have to figure out, “Okay, do you climb?” And I tend to climb if I think it’s legitimate heat…

…[00:26:56] Patrick: As you start to dig on the layers of what’s driving marketplace businesses, consumer ones specifically, what tensions are healthy? There’s a lot of stakeholders in marketplaces, and not everyone can get the best of everything all the time. How do you like to look and investigate tensions inside of a network?

[00:27:13] Jeff: Tensions are great because there’s two sides or three sides, and there’s always tensions. It started at eBay. The sellers paid us, and so the obvious thing, give the sellers what they want. But it turned out for me, what made eBay work was the buyers. Amazon and Yahoo both launched auction products early at eBay. By the way, they were the gorillas at the time. Particularly Yahoo. It was a $100 billion dollar market cap early. They launch auctions, they make it free. We charged a list. They made it free. Amazon made it free. And they quickly got millions of listings, but what they lacked were buyers. And so the sellers went there and it was like they put up billboards and no one walked by. And so they came running back to eBay and redoubled, their efforts on our platform. Long had the philosophy that why the sellers came is we had a robust buyer base, and so then growing the business requires optimizing the buyers’ base.

And so eBay and OpenTable, we did things that the sellers, the business side didn’t like. They viewed reviews on OpenTable. At OpenTable. I have four web windows open. I have the one for OpenTable, I have one for our map because we didn’t have a map. I had one for Zagat and Yelp because there were no reviews. And you’re like, “Okay, I think I see the path forward here, provide an integrated experience.” So we go to the restaurant and say, “Yeah, we’re going to debut reviews.” And they go, “You cannot publish a negative review from a customer I don’t know. You’re my technology provider. What are you doing?” Kind of thing. And you’re just like, “We did research. If a customer opened a review, they were twice as likely to make a reservation.”

You’re working with them. And then finally if we couldn’t convince them, we gave them the ability to opt out. “We will not show reviews on your page if you don’t want it. Just know that every other restaurant’s going to have reviews and you’re going to look pretty stupid.” So there are always those tensions. I almost always bias towards the buyer side of the equation. People come to Airbnb, hosts come to Airbnb because it has the largest guest network in the world. The more guests you have, the happier the host will be in the long term. You’re kind of optimizing for buyers, for diners, for guests, and in spite of the fact that the other side’s typically the one paying you, do

[00:29:22] Patrick: Do you have good examples of when the supply side is actually the harder side of the network? I remember talking to Gurley about this and saying, “Usually if you get all the buyers, the supply will show up.” But I’m sure there’s some examples where it’s different.

[00:29:35] Jeff: Airbnb’s been supply constrained almost since I got involved in the company. The supply’s expanding, but they believe they’d do more business if they had more supply, high quality supply. So it is interesting. Particularly in the unconventional businesses… I’ve said this probably. The first time I heard the Airbnb concept I said, “That’s the stupidest thing I’ve ever heard.”

[00:29:54] Patrick: So many, yeah.

[00:29:56] Jeff: I’m intensely private. I don’t want someone on my house, a stranger in my house. I don’t want to be in a stranger’s house. It was just like, “Oh.” When it’s that counterintuitive, the supply side, evangelical people to kind of say, “I see it, and I enjoy it.” And Airbnb was part economic empowerment but also part human relationships. They’re people who like meeting strangers and talking to them and learning about them and figuring out… There are multiple satisfactions involved in that experience. But there are a lot of marketplaces, particularly the weird ones, that can definitely use more supply…

…[00:35:35] Patrick: How far into the evolution of one of these marketplaces do you think it’s really important to start honing in on, I guess I’ll call it unit economics or margins, or something like on DoorDash or something? For a long time it was, “Well look, at scale these will be amazing.” And it’s kind of nebulous, what scale meant and when that would be. How much do you think about maybe the margin profile of a marketplace as you’re investing, especially if it’s early on?

[00:35:58] Jeff: I don’t not look at it if the margin’s not bad. An extreme case of this was Instacart. The time we invested, he was earning something like $12 a order in money to Instacart, and he was spending about $30. And so-

[00:36:16] Patrick: That’s a pretty bad margin.

[00:36:18] Jeff: That was pretty bad. And so the work I did that weekend was around profitability. And it turned out that he was just starting to do deals with grocery stores where the grocers would give him better pricing and share some of the incremental revenue from the economics. And that, at scale, would dramatically improve his economics. So one is you had to believe he’d get to the deals with the grocers. And then could he get to price parity? And he laid out this waterfall of, “This is how I’m going to make money.” And it was very detailed. Apoorva’s superpower is optimization. He’s just said, “These are the 19 things we need to accomplish to make the unit economics work. And I’m halfway on this one and just laid it out.” And I haven’t looked at that sheet in a while, but it largely came true. He made the unit economics work.

The big swing was he got the deals with the grocers and then the advertising business, I think Fiji, just announced it would be over $1 billion this year. Amazon showed that’s very high margin income. So the existence of that ad business means he can provide a very compelling value prop to the consumer because they don’t have to pay the full fare for the delivery. They get it partially subsidized through the advertising venue. And so that’s been key to the working, but the economics were awful when we invested. And so the leap of faith there wasn’t people would want groceries delivered to their homes. The leap of faith was he can make the economics work.

4. ‘Go for the Jugular’ – Sebastian Mallaby

On Tuesday, September 15, the pound took another beating. Spain’s finance minister telephoned Norman Lamont, his British counterpart, to ask him how things were. “Awful,” Lamont answered.

That evening Lamont convened a meeting with Robin Leigh-Pemberton, the governor of the Bank of England. The two men agreed that the central bank should buy the pound aggressively the next morning. As the meeting wound down, Leigh-Pemberton read out a message from his press office. Helmut Schlesinger, the president of the German Bundesbank, had given an interview to the Wall Street Journal and a German financial newspaper, Handelsblatt. According to a news agency report on his remarks, Schlesinger believed there would have to be a broad realignment of Europe’s currencies.

Lamont was stunned. Schlesinger’s remark was tantamount to calling for the pound to devalue. Already his public statements had triggered an assault on Italy’s lira. Now the German central banker  was attacking Britain. Lamont asked Leigh-Pemberton to call Schlesinger immediately, overruling Leigh-Pemberton’s concern that the punctilious Bundesbanker did not like to have his dinner interrupted.

After several conversations, Leigh-Pemberton reported that Schlesinger believed there was no cause for alarm. His comments were not “authorized,” and he would check the article and issue an appropriate statement when he reached his office in the morning. Lamont protested that this was a dangerously leisurely response. Schlesinger’s purported comments were already on news wires; traders in New York and Asia would react overnight; Schlesinger needed to issue a denial quickly. But Germany’s monetary master refused to be hurried. He was not going to adapt to a world of 24-hour trading.

That night, Lamont went to bed knowing that the next day would be difficult. But he could not imagine how difficult.

Stan Druckenmiller, the chief portfolio manager at George Soros’s Quantum Fund, read Schlesinger’s comments on Tuesday afternoon in New York. He didn’t care whether they were “authorized;” he reacted immediately. Schlesinger had made it obvious that the Bundesbank was not going to help the pound cling onto its position inside the exchange-rate mechanism by cutting German interest rates. The devaluation of sterling was now all but inevitable.

Druckenmiller walked into Soros’s office and told him it was time to move. He had held a $1.5 billion bet against the pound since August, but now the endgame was coming and he would build on the position steadily.

Soros listened and looked puzzled. “That doesn’t make sense,” he objected.

“What do you mean?” Druckenmiller asked.

Well, Soros responded, if the Schlesinger quotes were accurate, why just build steadily? “Go for the jugular,” Soros advised him.

Druckenmiller could see that Soros was right: Indeed, this was the man’s genius. Druckenmiller had done the analysis, understood the politics, and seen the trigger for the trade; but Soros was the one who sensed that this was the moment to go nuclear. When you knew you were right, there was no such thing as betting too much. You piled on as hard as possible.

5. Tracy Alloway — Understanding Financial Crises (EP.104) – Jim O’Shaughnessy, Jamie Catherwood, and Tracy Alloway

Jamie Catherwood: What’s your process for learning those new things in each kind of major crisis? How do you approach going from no knowledge of plumbing or commodities kind of nitty gritty details today to being able to talk about it?

Tracy Alloway: So this is one of the reasons I really like the podcast format. And this is one of the things that we do on all thoughts quite a lot is we try to go as micro as possible. So if we know that there are supply chain issues, we will talk to people who are into trucking, people who are into shipping, people who are experts in the world of wooden pallets, which I didn’t know we had experts on wooden pallets, but it turns out we do. We have on the economics of nails, experts on trust plates, lumber, the list goes on and on and on, but we’ll try to talk to those people as much as possible to get a handle of what’s going on in their individual markets so that we can connect that back to the macro.

Jim O’Shaughnessy: That’s nice try by the way there, Jamie, trying to get her to subscribe to Investor Amnesia. I like it, always, always

Tracy Alloway: I am subscribed.

Jim O’Shaughnessy: [crosstalk]. Look at that. I got the pull quote for you though, Jamie. I do a lot of research for our guests. And then I also have a couple of colleagues who do research as well. And I love this quote that somehow got connected to you. And it’s on this idea, it’s a tale quote, which it’s basically, it’s much easier to be a macro bullshitter than a micro bullshitter, right?

Tracy Alloway: I’m not sure why that quote’s connected to me, but I do like it. I like it a lot. I think there’s a kernel of truth there, which is, you see a lot of prognosticators, a lot of forecasters who will come out and say, “The economy’s going to do this, inflation’s going to do this.” I give this eventuality a 40% chance, which is the ultimate MBS of prognostication. And with the macro, it feels like there are so many variables swirling around that. You always have an excuse if you’re off, right? Well inflation, maybe it was transitory, but now there’s possibly World War III with Russia and that’s led to more supply shocks.

Tracy Alloway: So really I was right. It was going to come down, but no one could have predicted that Russia was going to invade Ukraine. You see that all the time. With the micro, it is the ultimate expression of individual expertise. And if someone is living and breathing a sphere like wooden pallets or nails, the economic contribution of nails throughout history, they know that market. And if they fail to predict which way it’s turning, I feel like that’s really like, they have skin in the for something like that. So that’s why we really enjoy talking to the micro people. We enjoy talking to the macro people too, but you get different things from each group…

…Jim O’Shaughnessy: Switching gears a little bit here. What can you tell me about fancy chickens?

Tracy Alloway: I have an inordinate amount of interest in the subject of fancy chickens. I don’t know, my dream is to, Jamie knows this, one day I will own so fancy backyard chickens and they’ll be beautiful. And my dog Pablo will chase them around. And Jamie, your dog is invited too. So the reason I took an interest in chickens is I’m just interested in chickens, but there’s actually a really interesting financial history nugget that comes out of reading and researching about chickens, which is, there is a massive –

Jamie Catherwood: Great pun by the way, [inaudible] nugget talking about chickens.

Tracy Alloway: There we go. So in the late 1800s or mid 1800s, there was a massive chicken bubble driven by this Victorian fashion for having chickens. So the world was opening up. There was lots of travel. There was lots of exploration. People started discovering that you can go to Indonesia and find this really cool looking chicken and bring it back to London and sell it for a lot of money. So this industry of collecting and breeding chickens became a thing. Price has became absolutely crazy. There are pamphlets written about this. People saying how ridiculous it was that people were spending money on birds and objectively, there are a lot of weird financial bubbles that have occurred throughout human history, but chickens is probably one of the weirder ones alongside maybe be rabbits in Japan and things like that, beanie babies.

Tracy Alloway: But there’s actually a really interesting outcome of the chicken bubble, which is that it gave the raw materials for research to Charles Darwin, right? When he was really starting to think about evolution. So suddenly he was surrounded by all these wild chickens that had been brought in from Indonesia or Asia. And he was able to breed them with domestic chickens in Europe and then say, well, they can breed together. So they must be related even though they’re from opposite sides of the world, there’s a link here. And that was one of the foundational pieces of research to his theory of evolution that would come out a few years later. So, whenever we talk about economic bubbles, we usually talk about the economic damage that they reek on the rest of the world. But in this one instance, we can say that actually something useful came out of the crazy Victorian chicken bubble.

Jamie Catherwood: Well, one thing I wanted to mention, I think it was in Liverpool or Manchester when they, in the 1830s or ’40s when they unveiled the first railroad mine in one of those cities. I think the mayor took the first trip and died on the ride. I feel like I’m missing [crosstalk].

Tracy Alloway: Great advertising.

Jamie Catherwood: Something along those lines of, because people were worried. And it wasn’t really until, I think Queen Victoria rode the train that people kind of really trusted that it wouldn’t kill them. Because there’s some quotes of scientist saying that, that wouldn’t work because people will die of asphyxiation just because you’re going at such high speed.

Jim O’Shaughnessy: The high speeds, like 40 miles an hour.

Jamie Catherwood: Exactly. So it was just funny, the grand unveil makes someone trust in the railroads and dies in railroad.

Jim O’Shaughnessy: Let me pick up on that because I think that is something I’d like Tracy’s viewpoint on. What Jamie just said basically was a well-known and well trusted personage in this instance, Queen Victoria associated herself with the railroad and then suddenly everyone is like, “Okay, that’s good.” Is that possible anymore? Or have we so atomized the world that the Queen of England associating herself with something we’d be just like, whatever.

Tracy Alloway: So this is something that I think about a lot, which is one of our very early episodes was with an archeologist called Arthur Demarest, who he’s often described as the real Indiana Jones of archeology, he’s out in Guatemala or wherever digging pits. And I don’t know, finding offs snakes and that sort of thing. But he came on a couple times really in the early days of Odd Lots to talk about his research into the collapse of civilizations. And the thing that he pinpoints a lot of collapses on, particularly in South America is this over extension into complexity.

Tracy Alloway: So the society has become too complex to function both on a sort of a societal level, the way people are interacting with each other, but also on a logistical and supply level. So the way the cities are actually supplied from outside and the difficulty of getting resources in as you get bigger and bigger, and this is something that I think about a lot. I think there’s a very fractious media environment. My dad’s American, I just got back from visiting him. We watched a lot of Fox News and other [inaudible] content. And I can tell you, it is polar opposite to what I’m seeing elsewhere and when you have an environment like that, it becomes very, very difficult to be on the same page and to have those conversations about what is possible and what’s reasonable.

6. A Few Beliefs – Morgan Housel

The worst financial decisions happen when people risk what they need in order to gain something they merely want.

Unsustainable things can last years or decades longer than people think.

Tell people what they want to hear and you can be wrong indefinitely without penalty…

…The luckier you are the nicer you should be.

Past performance increases confidence more than ability.

Define what you’re incapable of and stay away from it…

…Read fewer forecasts and more history…

…A lot of denial masquerades as patience.

A lot of people have a hard time distinguishing between what happened and what they think should have happened given their world view.

About once a decade people forget that bubbles form and burst about once a decade…

…With the right incentives, people can be led to believe and defend almost anything.

Expectations move slower than reality on the ground, so a lot of frustration comes from clinging to the trends of past eras…

…Progress happens too slowly to notice, setbacks happen too fast to ignore.

We are extrapolating machines in a world where nothing too good or too bad lasts indefinitely

Optimism and pessimism always overshoot because the only way to know the boundaries of either is to go a little bit past them.

The world is governed by probability, but people think in black and white, right or wrong – did it happen or did it not? – because it’s easier.

7. Twenty Lessons Learned – Michael Batnick

Nothing lasts forever. When growth stocks were going up every day, it felt like it would never end. Now that growth stocks are going down, it feels like it will never end. Everything ends, eventually…

…Risk management is most critical when it feels like you’re getting punished for managing risk.

Nothing is a perfect inflation hedge. Not gold, stocks, crypto, or cash…

…Diversification is the only answer to an unpredictable future. If everything is working, you’re not really diversified…

…Analogs are dangerous. We know how things played out in the past. That doesn’t tell us how things will play out in the future.

The more confident somebody seems, the more cautious you should be in taking their advice….

You didn’t know this was going to happen. You don’t know what’s going to happen next.

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Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. Of all the companies mentioned, we currently have a vested interest in Microsoft and PayPal. Holdings are subject to change at any time.

We’ve constantly been sharing a list of our recent reads in our weekly emails for The Good Investors.

Do subscribe for our weekly updates through the orange box in the blog (it’s on the side if you’re using a computer, and all the way at the bottom if you’re using mobile) – it’s free!

But since our readership-audience for The Good Investors is wider than our subscriber base, we think sharing the reading list regularly on the blog itself can benefit even more people. The articles we share touch on a wide range of topics, including investing, business, and the world in general.

Here are the articles for the week ending 08 May 2022:

1. An Interview with “Father of the iPod” Tony Fadell – Ben Thompson and Tony Fadell

Just to touch on that — I love that analogy, I’ll go back to it in a little bit — but the story of the iPod is so crazy. You weren’t even hired until April, yet you shipped in October. How was Apple able to move so quickly? Is there any company that could do that today? I doubt that even Apple could do that today with all their resources. How did that happen where you shipped this completely iconic product that didn’t even exist in the imagination of anyone, I guess in your imagination to an extent, but walk me through that process and how was that even possible?

TF: I think it was a coming together of a lot of things. The first one was experience. I and the people around me had experience for ten years. I pulled in a lot of people from either or General Magic or Philips or other people I just knew that I’d met around Silicon Valley over time. So one was having that network of being able to pull people in who knew what they were doing on this product, that was one thing.

Second thing was having a lot of failure before building these things, and they didn’t really necessarily become commercial successes, they might have been critical successes. So you had enough time doing this stuff. You’re like, “Okay, I’ve done this. I know to make boards. I know how to get software packages together, put all these things to happen.” So again, that was doing something totally new from a product perspective, but the process wasn’t necessarily new.

I think the other one was we had incredible leadership in Steve Jobs. He decreed from the minute after we gave the presentation to him in March of 2001, it was “Go!”. I had already been running it for a year before that doing MP3 players in my startup. So it was like, “Okay”, take all the latest knowledge I had gained during the contracting period, and ran with that.

And then the other one was we just cordoned off and it was, “Make it happen”. I saw so many projects that died at Philips because they didn’t happen fast enough, politics set in. So it was like, “Okay, we have to build this. We have to build it quickly. The holiday season’s coming around. This might be our one and only chance, because who knows when Sony’s going to come in and steal everything” because they were the number one in all audio categories. Every audio category Sony was number one in. So it was like, “Well they’re going to come for this”. So speed was everything.

So I had just been tempered all the time. One is technology changes, the market changes so quickly, you need to have the right experience and process, and we put it all together. Obviously, it was wonderful to have Apple in terms of the customer service angle, parts of the operations angle, but we had to do a lot of new stuff that Apple had never done before, and obviously the marketing, product marketing, pulling all that stuff together. So we got to pick the best bits of Apple and have them focused on us because of the leadership. Then we were able to build very quickly the new bits, throw them together, and just run like hell because at the end of the day, Apple isn’t the Apple you know it today. Twenty-one years ago, Apple was suffering. It had around barely 1% market share in the computer business, in just the US, that’s not worldwide, Apple wasn’t anywhere worldwide. It was only worth $4 or $5 billion, I think. maybe even $3 billion in total. Now it’s worth almost $3 trillion or $2 trillion, whatever it is this week.

So when you have leadership, when you have a competitor or at least you felt there was going to be a competitor coming very quickly, when the technology was there, right place, right time, and we had the right experience, and the company was at its wits end, because it had tried everything it could do to try to get the Mac to get back into the forefront of consumers’ minds with the iMac, whatever, and that wasn’t really going well. This was, “You’ve got to make it happen”, burn the boats, do whatever it takes to see this first product out there and even then it was a marginal success. It was a critical success. Everyone was like, “Wow!” but a lot of people were like, “I can’t buy it. It doesn’t work with my PC.” It didn’t work with Windows. We had to work really hard to make it a success.

It’s interesting that you list all those factors because in your book, when you talk about your experience at Nest, which I think the acquisition from Google maybe was a little more fraught and dramatic than you might have wished it could have been. But you had this contrast, you talk about Google antibodies resisting you and you said, “Oh, we had Apple antibodies resisting us, as well.” But the difference was Jobs protecting you and you also had this cultural bit where Apple needed a hit. To that end, I’m curious, are we reaching a point, a decade past his death, where Steve Jobs’ management abilities are actually becoming underrated? There are the scare stories that are still around. We all know he was this innovator in design, but just from being a manager and getting stuff out the door?

TF: Well, there’s one which is getting stuff out the door, and that’s a process and having good process. There’s another one, which is getting very innovative things out the door, things that are going against the grain of the internal business itself. The iPod was totally different than the computers at the time, “What? Apple’s making what? Stick to computers, Apple.” that’s what we heard from some people. So there’s leadership when you’re maintaining or when you’re operating something that’s already standing and working. And there’s another type of leadership when you’re trying to do something inside an organization that may be successful, may be not successful, but doing something very against the grain and seeing it through and saying, “We’re going to burn the boats. And this is the way it’s going to be.” That takes a different type of leadership and it takes what I always call is air cover. If we didn’t get what we needed, because we were on such a tight schedule, I could only call in the airstrike so often, but, “Steve. Need help.” and from above he would fly in and go, “Okay, what do I need to do?”

We do that a lot today with the businesses we work with, and we have to be the air cover for that and the investments we make and what have you. We fly in and go, “Okay, can we help you in some way? Where can we go to third parties or other ones to help you get what you need to start up this startup?” Leadership is really the key difference in all of this and understanding the difference between data-driven and opinion-based decisions. Steve was really great at understanding what were opinion-based decisions, and it was his opinion at the end of the day that was going to rule, and he was going to make sure everyone understood that “We’re going to do this. And yes, we don’t know if it’s going to be a success, but this is what I want done. Get it done, please.”

You talked about this in your book, actually, the opinion-driven versus data-driven decision making, and how to build the iPod in the first place was an opinion-driven decision, but to bring the iPod to Windows ended up being a data-driven decision. And in the case of an opinion, well it was Steve’s opinion that counted, but because it was a data decision, that’s how you were able to actually change Steve’s mind about going to Windows. Did I summarize that point properly?

TF: Yeah. It’s really correct. Look, Steve’s opinion specifically was at the beginning of the iPod project was, “We are going to make this amazing thing called the iPod” — we didn’t know it was called the iPod at that time — but “We’re going to make this thing, and this is going to drive Mac sales”. So to use the iPod, you’re going to have to buy a Mac, and that was his opinion.

Two years in, the numbers were okay for the Mac fanboys who had Macs, but no one else was interested in switching to a Mac just for an iPod. So we had that data and it showed very clearly that that original opinion or that hypothesis was that more people would buy Macs because the iPod was available was not right. We had a few people, but it was not this huge mass of people switching from Windows to the Mac because the iPod existed and you had to have one. So over time, and this is the third generation, we had to have the Windows connectivity, the Windows functionality, compatibility, to make sure it worked.

And then all of a sudden people were like, “Oh, this iPod thing is really cool. I’m using it on my Windows device, on my Windows laptop, or what have you. But I wonder what the full Apple experience would be?” And then people started buying Macs after they got a taste of the Apple experience with the iPod on their Windows based computer…

…A couple other points. Another interesting episode that we talked about privately was the bake-off at Apple when it came to the iPhone, if it was going to be iPod-based or if it was going to be touchscreen-based. One of the points you made in your book is that the bake-off was very short and it was resource constrained. You needed to make a decision, and then you invested in the right one. And I think this came up in the context of Facebook changing plans on their virtual reality OS. They had the Android one and they had their internal one, and it went on for years. Why do companies fall into this? Is it just that they’re too rich? They have too much money, and so they’re just undisciplined about this?

TF: Absolutely! That’s exactly the right thing, is when there’s too much money, there’s too many people saying that they can do it better, and there’s no time limit or other constraints, money limit, market constraints, what have you, these teams go at it. If you remember, there were two different operating systems going on at the time at Apple, before Steve got back,

That’s right. Yeah.

TF: There were pink and blue and all these things, IBM had it. So there were all these different kinds of in-fighting that happens, and it’s all based on constraints. When there’s a lack of constraints, that’s where all of these things bloom. At Google, when I was there, there were at least four different competing audio projects for audio in the home for playing music. There was four of them! I’m like, four? Why is there four? Everybody had a slightly different take and nobody was willing to go and kill them and prune them and say, “No, this is the right one,” and take all the pieces together, because they were too afraid, for whatever reason, I don’t know. It’s hard enough to have one great product that’s orthogonal to what the company does and saying, “Oh, this is an all new thing,” to have four of them, and say, “We’re going to launch all of them at some point?” That just doesn’t make any sense. Constraints are really key there.

2. Going Where Few Have Gone Before – Inside All Four Rolex Manufacturing Facilities – Benjamin Clymer

While Rolex’s manufacturing and design capabilities were (and still are) the reason that this company is so respected by its peers, it was Wilsdorf’s knack for storytelling that would would elevate Rolex to become the archetype of the luxury wristwatch not only for those within Switzerland, but also all over the world.

In 1927, Wilsdorf heard of a woman British woman named Mercedes Gleitze who had successfully swum the English Channel. Wilsdorf asked Gleitze to wear a Rolex Oyster watch around her neck as she swam. It should be noted that Gleitze had attempted this feat seven times before making it successfully, and then, due an attempt by another woman to steal the spotlight, was asked to swim it again. It was this last time that Gleitze wore the Rolex Oyster, not on her wrist but around her neck. 

She didn’t make it. After 10 hours in the freezing water, she was forced to abandon the attempt and be pulled into her trainer’s boat, because of numbness in her extremities. It didn’t matter, and Wilsdorf ran an ad in London’s Daily Mail citing not this most recent attempt, but Gleitze’s earlier successful attempt (which, of course, she swam without a Rolex). Still, her Oyster did withstand up to 10 hours in the bitter cold water of the English Channel, which was no small feat (you can read a detailed account here)…

…This, my friends, is where things get good. Plans-les-Ouates (an industrial park outside Geneva that’s also home to, among others, Piaget, Patek Philippe, and Vacheron Constantin) is where the Rolex of our collective imagination comes to reality – complete with robotic inventory machines straight out of Star Wars, a private gold foundry, and iris scanners. Built in 2006, Rolex Plan-les-Ouates is the largest of all Rolex facilities, comprising six different wings that are 65 meters long by 30 meters wide by 30 meters high, all linked by a central axis. I should also note that everything you can see from the outside of the building is actually less than half of what Rolex has here – the complex is 11 stories high, but you can only see five from the outside. The other six are underground and completely hidden from a casual observer’s eye, or the eyes of would-be competitors.

Here there are not only no cameras allowed inside, but we are also asked to surrender our mobile phones. This facility is, in my opinion, the core of Rolex’s competitive advantage and unlike any other Swiss (German, or Japanese) watchmaking facility on the planet. It may actually be completely unique in other industries too. I’ll explain why below.

Upon entry (and surrender of all digital device), we take a small elevator a few floors underground. The doors open to reveal what looks to be something akin to Dr. Evil’s underground lair, in the best possible way. The floor is cement, the hallways are wide. Access control points are everywhere – if someone doesn’t absolutely need to be in a particular room, then they simply do not have access to it. We immediately notice a gigantic elevator door – and when I say gigantic, I mean an elevator at a scale that I’ve never seen. I inquire about it – it can hold a load of up to five tons.

We are shuffled into a secure room – we are about to see the legendary Rolex automated stock system. Our guide places his eyes to the iris scanner (no lie) the doors slide open, and what we see is downright startling…

…Sorry guys. No photos allowed, nor provided. So what I will do is give you my best written description of what this absolutely extraordinary automated system looks like. There are two 12,000 cubic meter vaults, spliced by a network of rails totaling 1.5 kilometers, transporting over 2,800 trays of components per hour between the 60,000 storage compartments and the workshops upstairs. The view is straight out of Star Wars, minus the 1970s camp. This is efficiency defined.

Once someone within the workshops above requests a component, this incredible system takes just 6-8 minutes to retrieve it and deliver it to their work station. I remember when I was in undergraduate business school, our supply chain professional proclaimed Wal-Mart to be the model of professional logistics. I would almost guarantee you he said that because he’d never been to Rolex Plans-les-Ouates…

…Rolex owns its own foundry, where it creates its very own formulas for three different kinds of gold, and its own formulation of 904L stainless steel. Every single alloy used by Rolex is produced entirely in-house because, as they are quick to point out, the composition of the metal is the most important factor in determining a watch’s aesthetic, mechanical, and dimensional properties.

Rolex is able to make these special compounds because they have invested in something that few other watch companies would even dream of: a central laboratory with world-class experts in not only materials, but also tribology – the science of friction, lubrication, and wear – chemistry, and materials physics. This laboratory was truly extraordinary to see, and what was perhaps most impressive about the lab was not only the incredible testing going on, and the machines they’ve developed themselves (for example, Rolex invented a machine to open and close an Oyster bracelet clasp 1,000 times in a matter of minutes), but also the people who work there. I was asked not to mention from where Rolex has retained many of its top-tier scientists, but you can guess, and they are 100 percent not from the watch industry…

…I think what was perhaps most surprising about my visit to Chene-Bourg was the quality of gemstone and setting work Rolex does. I don’t really think of Rolex producing many watches with diamonds and stones, and they admit they don’t. But, this is Rolex and if they are going to do something, they are going to do it the Rolex way. This means 20 in-house gem setters, some of whom have names like Bulgari and Cartier on their resume. The stones they use? Only IF quality – otherwise known as “internally flawless” for those not familiar with jewelry-speak.

One of the coolest things I saw here was a machine that Rolex uses to filter the stones they receive for fakes, or anything that might not be what it’s supposed to be. One assumes that any supplier of Rolex understands just how big a business it is and might be tempted to take advantage of this, perhaps by including fake diamonds in with the real stones. Yes, well, Rolex has a machine in-house that can filter stones in mass to cull out anything that isn’t a real diamond. The machine costs tens of thousands of dollars so I asked how frequently they received a stone from a supplier that wasn’t an actual diamond. The answer? About one out of 10 million. They do it anyway, because this is Rolex.

3. Where Do Space, Time and Gravity Come From? – Steven Strogatz and Sean Carroll

Strogatz (02:56): It’s very exciting to me to be talking with the master of emergent space-time. Really mind-boggling stuff, I enjoyed your book very much. I hope you can help us make some sense of these really thorny and fascinating issues in, I’d say, at the frontiers of physics today.

Why are you guys, you physicists, worrying so much about space and time again? I thought Einstein took care of that for us a long time ago. What’s really missing?

Carroll (03:21): Yeah, you know, we think of relativity, the birth of relativity in the early 20th century, as a giant revolution in physics. But it was nothing compared to the quantum revolution that happened a few years later. Einstein helped the beginning of special relativity, which is the theory that says you can’t move faster than the speed of light, everything is measured relative to everything else in terms of velocities and positions and so forth. But still, there was no gravity in special relativity. That was 1905. And then 10 years later, after a lot of skull sweat and heavy lifting, Einstein came up with general relativity, where, he had been trying to put in gravity to special relativity, and he realized he needed a whole new approach, which was to let space-time be curved, to have a geometry, to be dynamical. It’s the fabric of space-time itself that responds to energy and mass, and that’s what we perceive as gravity.

(04:14) And as revolutionary as all that was, sort of replacing fundamental ideas that had come from Isaac Newton, both special relativity and general relativity were still fundamentally classical theories. You know, we sometimes prevaricate about the word “classical,” but usually what physicists mean is, the basic framework set down by Isaac Newton in which you have stuff, whether it’s particles or fields, or whatever. And that stuff is characterized by what it is, where it is, and then how it’s moving. So for a particle, that would be its position and its velocity, right? And then, from that, you can predict everything, and you can observe everything and it’s precise and it’s deterministic, and this gives us what we call the clockwork universe, right? You can predict everything. If you knew perfect information about the whole world, you would be what we call “Laplace’s demon,” and you’d be able to precisely predict the future and the past.

(05:08) But even general relativity, which says that space-time is curved, that still falls into that framework. It’s still a classical theory. And we all knew, once quantum mechanics came along, circa 1927, let’s say. It was bubbling up from 1900, and then sort of — it triumphed in 1927, at a famous conference, the fifth Solvay Conference, where Einstein and Bohr argued about what it all meant.

(05:32) But since then, we’ve accepted that quantum mechanics is a more fundamental version of how nature works. I know — you said this for all the right reasons, but it’s not that quantum mechanics happens at small scales. Quantum mechanics is the theory of how the world works. What happens at small scales is that classical mechanics fails. So you need quantum mechanics. Classical mechanics turns out to be a limit, an approximation, a little tiny baby version of quantum mechanics, but it’s not the fundamental one.

And since we discovered that, we have to take all of what we know about nature and fit it into this quantum mechanical framework. And we have been able to do that for literally everything we know about nature, except for gravity and curved space-time. We do not yet have a full, 100% reliable way of thinking about gravity from a quantum point of view…

…Strogatz (11:32): So I think that segues very nicely into the next thing I was going to ask you. We’re hoping, by the end of this episode, to give people a feeling of what it means for space-time to be emergent. But what would it mean for you, or anybody studying space and time, for them to be emergent?

Carroll (12:05): So I don’t think that there is any such thing as a position or a velocity of a particle. I think those are things you observe, when you measure it, they’re possible observational outcomes, but they’re not what is — okay, they’re not what truly exists. And if you extend that to gravity, you’re saying that what we call the geometry of space-time, or things like location in space, they don’t exist. They are some approximation that you get at the classical level in the right circumstances. And that’s a very deep conceptual shift that people kind of lose their way in very quickly.

(12:58) It’s a tricky word. We have to think about it. Emergence is kind of like morality. Sometimes we agree on it when we see it. But other times, we don’t even agree on what the word is supposed to mean. So, the physicists, and mathematicians, and other natural scientists tend to — but not always — rely on what a philosopher would call weak emergence. And weak emergence is basically a convenience, in some sense. The idea is that you have a comprehensive theory, you have a theory that works at some deep level. Let’s say, the standard example is gas in a box, okay? You have a box full of some gaseous substance, and it’s made of atoms and molecules, right? And that’s the microscopic theory. And you say that, okay, I could — in principle, I could be Laplace’s demon, I could predict whatever I want, I know exactly what’s going on.

(13:47) But, we human beings, when we look at the gas in the box with our eyeballs, or our thermometers, or whatever, we don’t see each individual atom or molecule, and its position and its velocity, we see what we call coarse-grained features of the system. So we see its temperature, its density, its velocity, its pressure, things like that. And the happy news — which is not at all obvious or necessary, it’s kind of mysterious when it happens and when it doesn’t — but the happy news is that we can invent a predictive theory of what the gas is going to do just based on those coarse-grained macroscopic observables. We have fluid mechanics, right? We can model things without knowing what every atom is doing. That’s emergence, when you have a set of properties that are only approximate and coarse-grained, that you can observe at the macroscopic level, and yet you can predict with them. And weak emergence just means, there’s nothing new that happened along the way. You didn’t say that, oh, when you go to the larger scales and you zoom out, fundamentally new essences or dynamics are coming in. It’s just sort of the collective behavior of the microscopic stuff. That’s weak emergence.

(15:01) There’s also strong emergence where spooky new stuff does come in. And people talk about the necessity of that when they think about consciousness or something like that. I’m not a believer in strong emergence at the fundamental level. So, to me, what the emergence of space-time means is that space-time itself is like, the fluid mechanics. It’s like gas temperature and pressure and things like that. It’s just a coarse-grained, high-level way of thinking about something more fundamental, which we’re trying to put our finger on.

Strogatz (15:34): Wow, as you’re describing the gas in a box, I happen to be sitting in a box. I’m in a studio that is kind of box-shaped. There is a gas in here, which is the air that I’m breathing.

So anyway, yeah, very vivid to me, the example you’re talking about. And it is amazing, isn’t it? That there are laws at that collective or emergent scale that work, that don’t — you know, like thermodynamics was oblivious to statistical physics. In fact, was discovered first, and only later, the microscopic picture came out. And so, I guess you’re saying something like that would be happening now with space and time and gravity, that we have the macroscopic theory that’s Einstein’s.

Carroll (16:14): When I’m not spending my research time studying quantum mechanics and gravity, I’m studying emergence. I think that there’s a lot to be done here, to be sort of cleaned up and better understood, in a set of questions that spans from philosophy to physics to politics and economics, not to mention biology and the origin of life. So, I think that these are deep questions that we’ve been kind of messy and sloppy about addressing, but I don’t think that the emergence of space-time is difficult for that reason.

(16:45) So, when you talk about, is the United States emergent from its citizens? Or is Apple Computer Company emergent from something? Those are hard questions. Those are like, tricky, like “where do you draw the boundary?”, etc. But for space-time, I think it’s actually pretty straightforward. The lesson, the important take-home point for the podcast is, you don’t start with space-time and quantize it, okay? Just like when you have the gas in the box, you’re trying to get a better and better theory of the gas in the box, but you realize that it’s made of something fundamentally different. And I think that’s what I’m proposing, and other people are proposing for space-time as well, that the whole thing that used to work for electromagnetism and particles and the Higgs boson and the Standard Model, where you started with some stuff and quantized it, that’s not going to be the way it’s going to happen for gravity and space-time. You’re going to have something fundamentally different at the deep micro-level, and then you’re going to emerge into what we know of as space-time.

Strogatz (17:46): Shouldn’t we start talking about entanglement, at this point, maybe?

Carroll (17:49): Never too early to start talking about entanglement.

Strogatz (17:51): Let’s talk about it. What is it? I hear it a lot. I hear quantum people talking about it. Nowadays, especially, with quantum computing, we keep hearing about entanglement. Why don’t you just start with telling us what it means, where the idea came from?

Carroll (18:04): Yeah, I mean, let’s think about the Higgs boson. We discovered it a few years ago, it’s a real particle, and I wrote a book about it, The Particle at the End of the Universe. The Higgs boson — one of the reasons why it’s hard to detect is that it decays. It has a very, very short lifetime, right? So, you can imagine if someone put a Higgs boson right in front of you, it would generally decay into other particles in about one zeptosecond. That’s 10-21 seconds. Very, very quickly.

(18:31) One thing it can do, it can decay into an electron and a positron, an antielectron. So it can decay into two particles, electron and positron. Now remember quantum mechanics. So, you can predict roughly how long it will take the Higgs boson to decay, but when it spits out that electron and positron, you can’t predict the direction in which they’re going to move.

(18:54) I mean, that makes perfect sense because the Higgs boson itself is just a point. It has no directionality in space. So there’s some probability of seeing the electron, in a cloud chamber or whatever, moving in whatever direction you want. Likewise, for the positron, there’s some probability, seeing it moving in whatever direction you want. But you want momentum to be conserved. So you don’t want the Higgs boson sitting there, stationary, to decay into an electron and a positron both moving rapidly in the same direction. That would be a shift in the momentum, right?

(19:26) So, even though you don’t know what direction the electron is going to move in, and you don’t know what direction the positron is going to move in — sorry, I’m already, I’m being, I’m being the person who I make fun of, I’m speaking as if these are real. Even though you don’t know what direction you will measure the electron to be moving in, and you don’t know what direction you will measure the positron to be moving in, you know that if you measure them both, they will be back to back. Because they need to have equal and opposite momentum, for those to cancel out.

(19:54) So what that means is, if you believe all those things, right away, this is why we believe there’s only one wavefunction for the combined system of the electron and the positron. It’s not an independent question, what direction are you going to measure the electron in? What direction are you going to measure the positron in? It’s a statement you need to ask at the same time. That’s entanglement, right there. Entanglement is the fact that you cannot separately and independently predict what the observational outcome is going to be for the electron and the positron.

(20:26) And this is completely generic and everywhere in quantum mechanics. It’s not a rare, special thing. Many things are entangled with many other things. It’s the unique and fun and very useful time when things are not entangled with each other. It took a long time — like, Einstein and his friends — Einstein, Podolsky and Rosen, EPR — published a paper in 1935 that really pointed out the significance of entanglement. Because it was sort of there, already, implicit in the equations, but no one had really shone a flashlight on it, and that’s what Einstein did. And the reason why it bothered him is because when that Higgs boson decays and the positron and the electron move off in opposite directions, you can wait a long time, let’s say you wait a few years before you measure what direction the electron is moving in.

(21:14) So, both particles are very, very far away from each other. And now when you measure the location of one, supposedly the location of the other one is instantly determined. And there’s no limit of the speed of light or anything like that. So for obvious reasons, Einstein, very fond at the speed of light as a limit on things, he didn’t like that. He never really quite thought that that was the final answer, he was always searching for something better.

Strogatz (21:39): And the argument goes nowadays that it’s okay, it’s no violation of special relativity, because you can’t use this to transfer any information or something? Is that the statement?

Carroll (21:39): Yeah, well, you know, there’s, there’s a whole bunch of statements that one can make. But the one that we absolutely think is true, is the one that you just made. If you imagine these two particles moving back-to-back, and one person detects one, and there’s another one, you know, a light-year away, who’s going to detect the other one, the point is that they don’t know what your measurement outcome is, you would have to tell them.

So even though in the global point of view, now, the location where the other particle is going to be detected is known to God, or to the universe, it is not known to any particular person sitting at any location within the universe. It takes the speed of light time to take a signal that would let you know that there is some now new fact about the matter, where you’re going to observe the positron. So, you cannot actually use this for signaling, you just don’t know what has happened when your other observer has measured something. And you can actually prove that, under reasonable assumptions, in the theory as we know it.

(22:43) So it seems as if this is the tension, that the way the universe works involves correlations that travel faster than the speed of light, but in some well-defined sense, information does not travel faster than the speed of light. That should worry you, that we didn’t define any of these words. So you know, what does that mean? You’re not going to build a transporter beam or anything like that out of this stuff.

(23:09) But — but let me just add one other thought, which I think, again, is a result of my quirky way of thinking about these things, which is not entirely standard, which is, people really like locality. Like, locality is a central thing. Locality is just the idea that if I poke the universe at one point in space-time, the effects of that poke will happen at that point, and then they will ripple out. But they will ripple out to other points no faster than the speed of light, okay? There’s nothing I can do to poke the universe here that will change the state of the universe in a tangible way very, very far away. And you can see how this entanglement thing is kind of on the boundary of that, like, the description of the universe changes instantly far away, but no information is traveling.

(23:51) So then, if you believe that locality is fundamental like that, then you’re sort of asking this question, why does the universe almost violate that but seem to not quite? That’s the puzzle that we have. And this is — a lot of ink has been spilled in the foundations of quantum mechanics.

(24:06) I think about it entirely the other way around, because I think of the wavefunction as the fundamental thing, right? I think that’s what exists in reality. And the wavefunction, like the wavefunction of this positron and electron is utterly nonlocal. It just exists all — it’s a, it’s a feature of the universe as a whole right from the start. So, I also have a mystery to be explained, but my mystery is the opposite way. It’s not “why is locality approximately or, you know, seemingly violated by entanglement?” It’s “why is there locality at all?” Like, that’s the puzzle to me.

4. Nvidia: The Machine Learning Company (2006-2022) – Benjamin Gilbert and David Rosenthal 

Ben: This was occurring to me as I was watching Jensen ensuring the omniverse vision for NVIDIA and realizing NVIDIA has really built all the building blocks—the hardware, the software for developers to use that hardware, all the user-facing software now, and services to simulate everything in our physical world with an unbelievably efficient and powerful GPU architecture.

These building blocks, listeners, aren’t just for gamers anymore. They are making it possible to recreate the real world in a digital twin to do things like predict airflow over a wing, simulate cell interaction to quickly discover new drugs without ever once touching a petri dish, or even model and predict how climate change will play out precisely.

There is so much to unpack here, especially in how NVIDIA went from making commodity graphics cards to now owning the whole stack in industries from gaming, to enterprise data centers, to scientific computing, and now even basically off-the-shelf self-driving car architecture for manufacturers.

At the scale that they’re operating at, these improvements that they are making are literally unfathomable to the human mind. Just to illustrate, if you are training one single speech recognition machine learning model these days—just one model—the number of math operations like adding or multiplying to accomplish it is actually greater than the number of grains of sand on the earth.

David: I know exactly what part of the research you got that from because I read the same thing and I was like, you got to be freaking kidding me.

Ben: Isn’t that nuts? There’s nothing better in all of the research that you and I both did to better illustrate just the unbelievable scale of data and computing required to accomplish the stuff that they’re accomplishing and how unfathomably small all of these are the fact that this happens on one graphics card.

David: Yeah, so great…

…Ben: It’s funny because that feels like that’s the killer use case, but that’s just the easiest use case. That’s the most obvious, well-labeled data set that these models don’t have to be amazingly good because they’re not generating unique output. They’re just assisting and making something more efficient.

Then flash forward 10 more years and now we’re in these crazy transform models with, I don’t know if it’s hundreds of millions or billions of parameters. Things that we thought only humans could do are now being done by machines and it’s happening faster than ever. I think to your point, David, it’s like, oh, there was this big cash cow enabled by neural networks and deep learning in advertising. Sure, but that was just the easy stuff.

David: Right. That was necessary though. This was finally the market that enabled the building of scale in the building of technology to do this. In the Ben Thompson, Jensen interview, Ben actually says this, when he realizing this talking to Jensen says, this is Ben talking, “The way value accrues on the internet in a world of zero marginal costs where there’s just an explosion in abundance of content, that value accrues to those who help you navigate the content.” He’s talking about aggregation theory.

Then he says, “What I’m hearing from you, Jensen, is that, yes, the value accrues to people to help you navigate that content, but someone has to make the chips and the software so that they can do that effectively. It used to be that Windows was the consumer-facing layer and Intel was the other piece of the Wintel monopoly. This is Google, and Facebook, and a whole list of other companies on the consumer side, and they’re all dependent on NVIDIA. That sounds like a pretty good place to be.” And indeed, it was a pretty good place to be.

Ben: Amazing place to be.

David: Oh my gosh. The thing is, the market did not realize this for years. I didn’t realize this and you probably didn’t realize this. We were the class of people working in tech as venture capitalists that should have.

Ben: Do you know the Marc Andreessen quote?

David: Oh, no.

Ben: Oh, this is awesome. Okay, it’s a couple years later, so it’s getting more obvious, but it’s 2016. Marc Andreessen gave an interview. He said, “We’ve been investing in a lot of companies applying deep learning to many areas, and every single one effectively comes in building on NVIDIA’s platforms. It’s like when people were all building on Windows in the ’90s we’re all building on the iPhone in the late 2000s.” Then he says, “For fun, our firm has an internal game of what public companies we’d invest in if we were a hedge fund. We’d put in all of our money to NVIDIA.”

David: It was a paradigm that called all of their capital in one of their funds and put it into Bitcoin when it was like $3000 a coin or something like that. We also have been doing this. Literally, NVIDIA stock—this is now 2012, 2013, 2014, 2015—doesn’t trade above $5 a share. NVIDIA today as we record this is I think about $220 a share. The high in the past year has been well over $300. If you realized what was going on, and again, in a lot of those years, it was not that hard to realize what was going on, wow, it was huge.

Ben: It’s funny. We’ll get to what happened in 2017 and 2018 with crypto and a little bit, but there was a massive stock run up to like $65 a share in 2018. Even as late as I think the very beginning of 2019, you could have gotten it. I tweeted this, and we’ll put the graph on the screen in the YouTube version here. You could have gotten it in that crash for $34 a share in 2019. If you zoom out on that graph, which is the next tweet here, you can see that in retrospect, that little crash just looks like nothing. You don’t even pay attention to it in the crazy run up that they had to $350 or whatever their all time high was.

David: Yeah. It’s wild. A few more wild things about this. AlexNet happened in 2012. It’s not until 2016 that NVIDIA gets back to the $20 billion market cap peak that they were in 2007, when they were just a gaming company. That’s almost 10 years.

Ben: I really hadn’t thought about it the way that you’re describing it. The breakthrough happened in 2010, 2011, 2012. Lots of people had the opportunity, especially because freaking Jensen is talking about it on stage. He’s talking about our earnings calls at this point.

David: He’s not keeping this a secret.

Ben: No, he’s trying to tell us all that this is the future. People are still skeptical. Everyone’s not rushing to buy the stock. We’re watching this freaking magic happen using their hardware, using their software on top of it. Even semiconductor analysts who are like students of listening to Jensen talk and following the space very closely think he sounds like a crazy person when he’s up there espousing that the future is neural networks, and we’re going to go all in. We’re not pivoting the business, but from the amount of attention that he’s giving in earnings calls to this versus the gaming. I mean, everyone’s just like, are you off your rocker?

David: I think people have just lost trust and interest. There were so many years, they were so early with CUDA and early takeout. They didn’t even know that AlexNet was going to happen. Jensen felt like the GPU platform could enable things that the CPU paradigm could not, and he really had this faith that something would happen. He didn’t know this was going to happen. For years, he was just saying that we’re building it, they will come.

Ben: To be more specific, it was that, well, look, the GPU has accelerated the graphics workload. We’ve taken the graphics workload off of the CPU. The CPU is great. It’s your primary workhorse for all sorts of flexible stuff. But we know graphics need to happen in its own separate environment, have all these fancy fans on it, and get super cooled. It needs these matrix transforms. The math that needs to be done is matrix multiplication.

There was starting to be this belief that like, oh, well, because the apocryphal professor told me that he was able to use this program that matrix transforms to work for him, baybe this matrix math is really useful for other stuff. Sure, it was for scientific computing. Then, honestly, it fell so hard into NVIDIA’s lap that the thing that made deep learning work was massively parallelized matrix math. NVIDIA is just staring down their GPUs like, I think we have exactly what you are looking for.

David: Yes. There’s that same interview with Bryan Catanzaro. When all this happened, he says, “Deep learning happened to be the most important of all applications that need high throughput computation.” Understatement of the century. Once NVIDIA saw that, it was basically instant. The whole company just latched on to it.

There are so many things to laud Jensen for. He was painting a vision for the future, but he was paying very close attention, and the company was paying very close attention to anything that was happening. Then when they saw that this was happening, they were not asleep at the switch.

Ben: Yeah, 100%. It’s interesting thinking about the fact that in some ways, it feels like an accident of history. In some ways, it feels so intentional, that graphics are an embarrassingly parallel problem because every pixel on a screen is unique. You don’t have a core to drive every pixel on the screen. There are only 10,000 cores on the most recent NVIDIA graphics cards, but there’s not, which is crazy, but there are way more pixels on the screen.

They’re not all doing every single pixel at the same time every clock iteration. But it worked out so well that neural networks also can be done entirely in parallel like that where every single computation that is done is independent of all the other computations that need to be done, so they also can be done on this super parallel set of cores.

You got to wonder, when you kind of reduce all this stuff to just math, it is interesting that these are two very large applications of the same type of math in the search space of the world of what other problems can we solve with parallel matrix multiplication? There may be more, there may even be bigger markets out there.

5. Twitter thread on an interview of Ted Weschler – Thomas Chua

1. Who is Ted Weschler? He was the founder and managing partner of Peninsula Capital Advisors. Between 1999 and 2011, the $2B fund returned 1,236% to its investors. He wanted to meet his hero and so he bided on the annual auction lunch with Buffett.

2. One fateful Tuesday morning, he received a phone call that changed his life. It was Buffett on the other end. He had won the annual charity auction lunch. Ted flew out to Omaha two days later to meet his hero. Everything clicked. Ted bid again the following year and won!

3. This time, Warren asked him:  “I think you’d be a pretty good fit out here. Would you have any interest in working at Berkshire?” He panicked. On one hand, he was running a successful fund and his family was in Charlottesville. But on the other, this is Warren Buffett!

4. He wrote Buffett a letter when he got back to Charlottesville explaining that it was difficult because his family was rooted here. Buffett replied “You can manage money from the moon as far as I am concerned.” Buffett was a real pioneer in the work from home trend 😂…

…7. Investing is a game of connecting the dots. We want to build up a lot of data in our minds and understand why the business will be vastly different five years from now than what the market perceives. He reads trade journals regularly to understand businesses…

…9. Why he always feel positive? United States has a system that works. There’s will be negativity every now and then. But if you take a long-term view, there’s innovation coming out every day and it keeps getting better. It’s hard not to be optimistic.

6. Sources of Enduring Business Success – John Huber

I recently read through the letters of Nick Sleep, who ran a very successful investment fund in the United Kingdom before closing it last decade. Sleep is a great thinker and I highly recommend his work. One thing Sleep wrote a lot about is how the average holding time period for many of the stocks he owned was around 50 days, whereas he planned to hold these stocks for more than 250 weeks (5 years). I think his key observation is important: The marginal buyer who is holding a stock for 2 months is not placing much emphasis on that company’s competitive advantage because that advantage won’t matter much at all over the next few months; what matters over that period of time are things like market perception, news flow, sentiment, and perhaps short-term business momentum…

…So what Sleep did is he decided to compete in a different game. Instead of attempting to determine how the crowd will react this quarter or how the trajectory of the business will fare this year, he wanted to focus on the factors that contributed to a business’s ultimate potential. What attributes give this company an advantage? What will lead this company to success through both good times and bad times (because if you’re a long-term shareholder, all companies face headwinds at some point).

…Sleep used the example of Walmart’s cost advantage. Walmart’s business model was to offer the lowest prices on everyday merchandise, and steadily gain scale advantages through larger and larger bulk purchases from suppliers at lower and lower unit prices, which meant further savings to customers, which led to more growth and more scale advantages. Sleep coined a term for this business model: “scaled economies shared”, meaning the business gained scale, but instead of keeping the excess profits for itself, it gave these scale advantages to the customer in the form of lower prices. This sacrificed near term profits but led to far greater future profits, which of course is where value comes from.

Walmart, Costco, and Amazon all exhibit this basic business model, and all have achieved great success. But what Sleep noticed is that investors — even when they understood this business model — still undervalued all of these companies because they placed too much emphasis on shorter term factors such as seasonal same-store sales trends, quarterly margins, or the business cycle. All of this focus came at the expense of what really mattered, which was the cost advantage that was so hard for competitors to replicate….

…Last summer, investors sold Amazon after its Q2 earnings report because the next few quarters would face tough comps from the gangbuster 2020; but Amazon’s value in 2032 has little to do with the comps it faces in 2022. It has a lot to do with the durability of its network, the economies of scale, the distribution advantages, the culture of operational excellence; none of that will likely drive the stock this quarter, but it’s what matters most to the stock over the next decade.

A mismatch of time horizons lead some investors to more heavily weight the short-term and deemphasize these sources of “enduring business success”.

7. Twitter thread on how company leaders handle crises – Dan Rose

I was at Amzn early ’00s when we lost 95% of our market cap. Later at FB I negotiated a down-round in ’09, and then in ’12 our stock dropped 50% post-IPO. I was on the board of a public company that went bankrupt (Borders) and a start-up that went under (Hello). Some lessons:

1/Raise capital when you can, not when you need it. Amzn tapped convert debt in Feb ’00 – if we had waited another month we would not have survived. 9 years later at FB we raised a 33% down-round despite having plenty of runway. Don’t wait until your back is up against the wall

2/Cash is king. Forget about valuation, dilution, etc – if you run out of cash, none of it matters. Borders used its free cash flow to buy back stock for yrs, ignoring the internet. By the time a PE firm fired the board and asked me to join in ‘09, we had no runway for turnaround…

…4/Change the tone. Amzn did a small but symbolic RIF in 2000. Around that time, Jeff was presented with a team t-shirt – he threw the team out of his office and banned all company swag. We even removed aspirin from the break rooms, served coffee and water. Small acts set the tone

5/It starts from the top. Zuck showed up to work in Jan ’09 wearing a tie, and he wore it every day for an entire year. His message to the company: “it’s time to get serious about our business.” Every time we walked into a meeting with Mark, we were reminded things had changed

6/Reset the team. In the middle of covid I addressed the exec team of a travel start-up whose revenue dropped to zero overnight. I encouraged them to re-evaluate their team. Some people step up in a crisis – they are your future leaders. Others will jump ship – good riddance…

…9/Communicate, a lot. When FB’s stock plummeted after our IPO, I addressed the issue with employees rather than pretending stock price didn’t matter. It’s tempting to go into a foxhole when times are tough. Don’t do that, your team needs you more than ever

10/Keep telling your story. I stayed at Amzn during this time because Jeff sold me on his vision. When GFC postponed FB’s IPO by 4 years, Zuck never stopped talking about the mission. Churchill taught the world the power of storytelling in a crisis

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Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. Of all the companies mentioned, we currently have a vested interest in Alphabet (parent of Google), Amazon, Apple, Costco, and Meta Platforms (parent of Facebook). Holdings are subject to change at any time.

We’ve constantly been sharing a list of our recent reads in our weekly emails for The Good Investors.

Do subscribe for our weekly updates through the orange box in the blog (it’s on the side if you’re using a computer, and all the way at the bottom if you’re using mobile) – it’s free!

But since our readership-audience for The Good Investors is wider than our subscriber base, we think sharing the reading list regularly on the blog itself can benefit even more people. The articles we share touch on a wide range of topics, including investing, business, and the world in general.

Here are the articles for the week ending 01 May 2022:

1. Henry Ward – Transforming Private Markets – Patrick O’Shaughnessy and Henry Ward

[00:15:45] Patrick: I remember reading about CartaX when it was first announced or posted online several years ago, and thinking, “Wow, what an interesting way to sit on top of the cap table infrastructure that you’ve built to now provide a real secondary exchange for private markets, and the possibilities that might unlock.” How would you grade yourself so far? Maybe describe CartaX. I think I kind of just have described it in its basic terms, but I’ll let you describe it how you see it. And I’d love to hear how you think it’s gone. Are you satisfied with the scale of it? What have been the lessons or the challenges you’ve learned building something notoriously hard to build? Because a huge defacto one doesn’t exist like the NASDAQ or the NYSE or something like this for this market.

[00:16:25] Henry: We’re definitely in the new market creation business. One question to ask is how you define a new market. And our definition is anything where you’ve made a way for money to exchange hands that hasn’t happened before. One example of that is having cap tables. We weren’t the first cap table provider. We were the first cap table provider to charge companies. And as an example, in CartaX we’re the first exchange where we’re charging buyers and sellers commissions to trade and provide crossing trades for them as a service. These things are really hard to get going. But when they go, they accelerate very, very quickly. If I were to grade ourselves, I’d give us a B minus on CartaX. I think we’re attracting a lot of supply, and now we’re building up the demand side of the equation. All marketplaces start this way. So if you’re an investor looking at marketplace companies, all marketplaces start with this thesis that there’s hidden embedded demand that you can’t see. And then the marketplace has somehow figured out how to unlock supply.

So if you look at Airbnb, there’s embedded demand that people wanted to sleep on people’s couches, and then Airbnb figured out how to unlock that supply and get people to do it. And the same for us. I think we have figured out how to unlock supply. I think companies are coming to us at scale. We run 20 liquidity events a month these days to unlock supply and create liquidity on their cap tables. The challenge now is, as supply starts to rise, every marketplace has this question. How do you then have demand rise as well? And it’s always that balancing act. And I think we’re in the demand side of this equation, how do we attract more investors to CartaX so that they can start buying into these pre-IPO liquidity trades?…

...[00:25:26] Patrick: How do you make those kinds of decisions faced with an infrastructure that, like you said gives you optionality, enables you to build other stuff? How do you decide what’s a good idea and what’s not? So I’ll leave it at that. I mean, it just seems like when there’s lots of options. Sometimes it’s very difficult to know what to focus on. So as a manager of a business now abstracting away from the specific problems you’re solving, what do you think are the right ways for other entrepreneurs to think about that problem of where to focus?

[00:25:53] Henry: I took something from our friend, Mark Andreessen where he talked to me about Andreessen Horowitz. There’s no bad ideas. It’s only timing. And if you have that belief system, he’ll walk you through this history of ideas that happened that were before their time, but then actually ended up being a good idea. I can do it for [Webend] versus Instacart, but the Andreessen people can do it for the like last 150 years. So we’ve taken that. What’s so great about that model is the question isn’t what’s a good idea, what’s a bad idea. The question to it comes is now the time for this idea. And that’s such a different way to think about investment decisions. I love that framework. We’re not investors, we’re operators. So we have our own framework, which is there’s no bad ideas, the question is which aperture you look at for this idea. So if you’re looking at an aperture saying, “Hey, we’re trying to solve this one problem for a user. It feels like we should do this for this user.” If that aperture is correct, when you’re a product manager focused on your user and the user wants feature X, we should do that. But then if you look at it through a different aperture, let’s say what is core to our mission over the next 10 years that feature may not actually be important to us. And we’re both right.

So the question is which aperture do we look through? I’ll give you one really good example. If you talk to some of our CEOs that are customers, some of them don’t like to give vesting email reminders to employees. This is a really weird one. But they don’t like it because they don’t employees worried about equity. It’s also sometimes they don’t want employees if they leave to know that their options can get exercised. There’s some weird dynamics that happen with some companies and investing email reminders for employees. So if you look through the aperture of what do I do to make my customer happy? You might say, “We should turn that off if a customer wants that.” If you look at through the aperture of our goal is to normalize equity as a means of compensation and educate the world about equity, we absolutely would send everybody vesting reminders and teach them about how important it is that they exercise their options. And both are correct answers. The question is which aperture do we want to look through today? And that’s how we look at everything is what’s the right aperture to look through a decision and then make a decision through aperture? And my job as a CEO is not to opine on yes, no versus good ideas, bad ideas, my job is to help the executive team to figure out what’s the right aperture for decision-making.

[00:28:07] Patrick: There’s this great idea, that idea of 1 of N versus N of 1 companies that I think I first became enamored with through David Haber, another mutual friend of ours, also at Andreessen. And I think he might have credited you with this model. Maybe talk about that concept a little bit and whether it also applies to this product, this decision framework, not just at the company level, but down at the feature level.

[00:28:28] Henry: I am a huge fan of the 1 of N versus N of 1 framework. And I just have to give credit, I probably talk about it more than anybody else because I’m such a disciple, but this actually came from Arjun at Tribe Capital who you may know. He told me this framework and I just ran with it. So an N of 1 business is one where the market structure allows for a monopolistic effect where there can be one and one winner. The N of 1 winner. A one event market is one where there’s lots of competition. You almost think of it like Peter Thiel’s competition is for losers, right? He has a very black and white view of the world. There’s either perfect competition or there’s monopoly and there’s nothing in between. And we subscribe to that view. Our job is to never enter 1 of N markets. Never enter anything where the end state of this marketplace has to be one with multiple competitors and only enter markets where we have a real chance at becoming the N of 1 player. And that actually makes it tricky because when you enter new markets to be an N of 1 player, by definition, you have to go to relatively small markets because large markets are really hard to become an N of 1 player. It takes a lot of time. You have to have the scale to take over these N of 1 markets. Like Amazon is still not N of 1, but boy, are they heading that direction. That is the balancing act where the investors that invest in Carta, the criticism might be, “Hey, they only go after small markets.” The bullish case is, “Well, hey, but they win all these markets. Each time they win a new market, it gives them optionality to build on top of that market and go into something bigger.” And so far we’ve been able to execute against that strategy.

[00:30:02] Patrick: What are some of the key principles of how you run the company that map back onto that idea of N of 1 market company, whatever? What is different do you think about running a company that, that explicitly is the goal or the strategy is to just be in markets that they can dominate?

[00:30:19] Henry: Yeah. I used to have this conversation a lot with candidates that I was trying to recruit. Back in the early days, especially, I’d compete against Instacart and I’d compete against MongoDB, and Gusto, and payroll companies and house tech companies. What I would always tell them is I would say, “Hey…” When I had a candidate that had an offer from a payroll type company and an offer from me and they were trying to figure out the two, and I would say to them, “Hey, there’s two types of businesses that you can pick from. One is a business like this payroll company we’re competing with that has line of sight to…” At that time, a billion in revenue seems crazy. Now, I would say 10 billion in revenue. But they had a line of sight to a billion in revenue when they were a series A or B company. The question was, could they out execute? There’s a billion dollars easily available in TAM for a payroll company or for a database company. The question is can they just execute better than incumbents and get there and build a better, faster, cheaper product?

For us, we’ve never seen line of sight to a billion in revenue or 10 billion in revenue in any one product line. We’re like that company that sort of has a machete and we’re hacking our way through a foggy jungle, and we’re building the path as we go. The first type of company, I would call an execution company. They know exactly what to do. The question is, can they organize a team and execute against that plan better than anybody else? For us, we don’t know what to do. We have to keep innovating and finding new markets because in any existing market, we’re going to run out of oxygen and we’ve raised venture capital. We’ve raised too much money to just flatline. And so we constantly have to innovate and find new paths. And the question is which company do you want to work for? High performance execution team or an innovation discovery company where we’re constantly beating our own path? And for some employees, it was better to go to an execution company. I would say everybody that comes to Carta is here for the journey, not the destination, because we don’t know what the destination is.

[00:32:11] Patrick: Let’s just imagine there was two classes of five amazing job candidates, a designer and engineer, whatever the lineup was. We could run a sliding doors experiment. So that five-person group went to payroll company in one world and they went to Carta in another world. In what ways are those two paths in the actual experience of doing the work the most different? I understand the concept, but in practice, like in literal terms, what is different about those two paths or those two kinds of companies and therefore those kinds of employees and how they operate?

[00:32:41] Henry: I would say that the experience of the employee is a top down versus bottoms up management style. If you’re an execution company like a database company or a payroll company, they know exactly what to do. The roadmap is defined from the top. Execution is measured and progress measured, OKRs, all this kind of stuff. So they’re given the thing. Here’s what you got to go do, and you just have to go do it. It’s great and you’ll do it really well, and all that kind of stuff. I think employees that come out of those companies become great executives. Because how do you become a great executive? I call it deterministic management. You know exactly what to do. You have a roadmap, you have a plan, and your job is to operationalize that plan. And they become great executives. If you work for a company like ours, we have no idea what to do. It’s very bottoms up. We intentionally organize that the best ideas come from the bottom. And my job is not to actually make decisions on what to do because I’m not close enough to the customer, to the markets, to all these things. My job is to give people the framework that they make the decisions on what to do.

So for example my framework is only N of 1 markets. We only do new market creations, so we’re not going to try to invade an existing market, we’re going to find a way for money to exchange hands that hasn’t happened before and make that true. I give all the frameworks for how to make these decisions, but you really push decision-making to the bottom. And it feels like for employees is it’s scrappy, it’s exciting. It’s also incredibly chaotic and they have no idea what’s going on half the time. And I would say the best thing, if you want to be a founder, Carta is the right place to do this. If you want to be an executive, this is a terrible place to learn to be an executive. But if you want to be a founder, this is how you do startups. We have the Carta cartel, we affectionately call it, early stage employees that have left to do startups, and there’s a dozen of them already. We breed founders. That’s what we do here.

[00:34:20] Patrick: What do you do to make that so true? What is the empowerment that’s happening? What is being pushed down, I guess, to that bottom that allows that experience to happen for them, deliberately from you and leadership team?

[00:34:33] Henry: A big part of it is roadmaps and decision making is pushed all the way down to the people that matter. So we’re very good at allowing experimentation to take place. I’ll give you a very practical example of this, which is really hard, hard to figure out. Let’s say a director level or senior director, something, their project tried a new product or new thing, and it didn’t work out. And now their performance review is coming up. We do a four point rating. Four is the best, one is the worst. Do you give them a two because it didn’t work out or do you give them a four because they tried. That question is it seems so simple, but it’s such a fundamental question because if you give them a two, nobody will ever take risks because they’ll only do things they know that work. And if you give them a four, people will want to take risks because they know that they’ll get rewarded for that effort. We’re a company that gives fours. Most companies won’t. If you ask most companies, what do you do when somebody tries something and they fail at it? They’ll say, “Well, we’re an outcome-based company. Results matter.” We’re an input based company, not an output based company. The results will be the results. What we question is do we do it the right way?…

…[00:45:02] Patrick: Maybe say a word of what you’ve learned about… You’ve given a lot of these interesting management concepts. I’d love any interesting, similar concepts on product you’ve got now beyond cap table, a number of different things that you do for your customer. What does great product, especially in the world of software, mean to you? What are the characteristics of a great product for Carta, but even more generally?

[00:45:22] Henry: There’s this great image I shared with all my product managers. I’ll try to describe it, hopefully, in words. There’s two styles of product management if you want to build a car and the first one was this, iteration of how to get to a car in pieces. You start with a chassis and then you start with a wheels and then you start with a steering wheel and then you put in a steering wheel and you put in seats and at the end of this, you get a car. Then the other style of product management was you start with a skateboard and then a scooter. Then you put a stick on it, it becomes a scooter. And then it becomes an electric scooter. And then it becomes a go-kart and then you get to a car. I love that one because what’s so powerful about that is, the first version of this product has utility in the second style, but not in the first. And so we talk a lot about… Everybody wants to build a car. We know that’s what we want to do, but that’s not the hard part in product management. The hard part in product management is the path to the car and how do you provide utility along the way? This is one of the things that big companies get wrong a lot, because they have so many resources. They’re like, “We’ll just go straight to the car. We’ll build a chassis, we’ll build this.”

We have executives that know how to operationalize this. We have a roadmap and a plan, but if you’re in a discovery company where you’re not sure what this car is going to look like, you have to start with utility early. And this is why it works really well for founder-led companies, because that’s what venture is like. Nobody gave me a billion dollars to start Carta. I started with 200K and then a million and a half. And each way I had to show utility, I had to show something that at that stage of the company was sustainable, we could build off of, and big companies don’t have that. And so they do these massive projects that often fail three years in where we instill that deeply into our product teams, even now we’re 2000 employees, that your job is to build a scooter first and not the chassis.

[00:47:10] Patrick: I love that. It reminds me of one of my favorite books by this guy, John Gall called The Systems Bible. And one of the principles in The Systems Bible is there’s no such thing as a complex system that’s just designed complex and implemented. Every complex system that works evolved from a simple system that worked first, and that really makes me think of that skateboard-scooter-car way of thinking about product. Same question for teams. Define what a great team looks like, especially given that it sounds like you are really pushing the fate of the company down onto relatively small teams at the edge of the business, not from the top down. What does a great functional team look like in your opinion?

[00:47:47] Henry: I talk a lot about this with my execs, where I have this interpersonal theory about how people talk and work together and I call it process and content. Process is how you work together. Content is what you’re talking about. Most teams and management C-level execs talk a lot about content. What’s the right budget here? What’s the right product here? What should we do here? All of it is around the decision making and what’s the right decision? And I spend a lot of time with them, especially with execs that come from bigger companies where vigorous debate is good because it gets you to the right answer. Another management maxim I can’t stand is, debate is good. And what I talk to them about is what I care most about this process, how we work together, how we talk to each other. You’ll give this example where two execs are arguing and not getting along and upset about something, but they ended up getting to the right decision, to the right outcome and agreeing on it.

They would consider that success. I would consider that failure. I use this phrase. Friction is failure, and most people think friction is good, because it shows a healthy debate. And so to me, it’s in a great exec team, works really well together and is okay if they don’t get the right answer. My favorite lines that I learned about partnerships is, great partnerships work when the relationship matters more than the answer, and I think that’s true for teams. How we work together matters more than the answer and we’re okay making mistakes to preserve the collaboration of what we’re working on together.

[00:49:13] Patrick: I like this line of questioning around aspects of the business so I’ll keep going. What defines great in go-to market, whether that’s marketing sales? You can tell me what matters more for Carta. What have you learned about what great means at doing this part of the company building motion really well?

[00:49:28] Henry: For us, I have a very specific answer. I don’t know that I can speak for all companies, but definitely for us, we are in this, I would say, later innings transition, the moving from a single product company to a multi-product company and the platform, yes. Multi-product and I would even say multi customer, because we both sell cap table software and compensation benchmarking software to companies, but we also sell back office and fund administration to venture funds. If you look at any life cycle of a company, obviously they start with an idea and they’re trying to get to product market fit. That’s stage one, is trying to get to product market fit. Then after product market fit, most companies die before that ever happens. That’s the first wave of death. The second wave that happens is they get to product market fit, but they can’t scale effectively, and that’s stage two, which is how do you scale this product that’s seems to be working? A lot of companies die there, but much, much fewer. That tends to be a little bit easier. Getting to product market fit is the hardest part.

And then at some point, unless your database is your payroll, you’re going to run out of oxygen there, you’re going to have to have a new product or a new customer, expand the market and then it becomes a multi-product, multi-customer company or platform. Vast majority of companies die there. That’s where you get the single digit billion outcomes, $2 billion market cap and always will be. But if you want to get the 10, 20, 100 billion in market cap, you have to become multi-product. Being in the midst of doing it right now, that’s actually really hard to do. It’s harder than I would’ve thought. And building a GTM motion that becomes the pipelines of distribution where we can invent a new product, we can acquire through M&A, Corp Dev a new product and then push that through the lines of distribution to our customers in a scalable way. That’s really challenging, but the teams that can do that’s incredibly valuable because now, if you get a good product market fit and a lot of that can be experimented with outside, you just look at these startups, you see which one’s getting traction and you buy it and you push it through your pipes. That is how you do Salesforce-level execution.

2. RWH005: Meet The Master w/ Aswath Damodaran – William Green and Aswath Damodaran

William Green (00:06:38):

You ended up at UCLA, you have multiple degrees if I remember rightly, and I wanted to get a sense of how you stumbled into teaching, because it seems like everything you do really is about teaching whether it’s being a professor at NYU, making videos for YouTube, writing your blogs, writing your books. And so, I’m curious how you actually discovered this lifelong passion which… What, you’ve been teaching now for 40 odd years?

Aswath Damodaran (00:07:02):

42 years now. No, it was accidental. Like so many things in so many people’s lives, it was just being at the right place at the right time. I came to UCLA to do my MBA. At that time, I’d already got a Master’s in Business in India, but because I had only 15 years of education, in India, school runs through quicker, US universities then required 16 years. So basically, I had to come back for a second Master’s. And my intent was to do what all MBAs do, which is to go work for some place which pay me a lot of money. When I started in 1979, that one might have been a consulting firm. But by the time I got towards 1981 and getting close to graduation, I was hitting the start of the growth of Wall Street exploding out, where you saw investment banks hiring.

Aswath Damodaran (00:07:47):

And I was on the verge of accepting that position at an investment bank when I realized I had run out of money and I needed to do something just to get enough funds to make it through when my job started. So I took a job as a TA, a teaching assistant, for an accounting class, a subject, as you might know, I don’t particularly care for. But I needed the money. So I remember I said, “I’ll get this done. It’s a quarter. How much pain can it be?” So I still remember that first day I walked into the class, and I was nervous. I mean, like everybody is when you’re in front of a big group of people. At about 15 minutes in, I don’t know what it was, but I realized that this was what I wanted to do with the rest of my life.

Aswath Damodaran (00:08:26):

I’m not a religious person, but I do believe that you get these moments of clarity when, I don’t know, some supreme being is saying, “Hey, listen, this is what you were meant to do.” I was lucky to be listening. And that moment changed my life because I said… And I remember right after that class, I marched up to the floor of the finance department, talked to professors there about, “Hey, how can I get into the PhD program? I want to be a teacher.” And luckily, that path opened up and I became a PhD. And the rest of my life has been all about teaching.

William Green (00:08:57):

I remember you once describing that as a [Godshot 00:09:00], which I thought was a wonderful phrase to describe that kind of 15 minutes that change your life. I am sort of a mystic who pretends to be rational because I cover the investing world where you’re supposed to be rational. So, I kind of love the idea that somehow there is some sense in which we’re being guided in life. I have no rational or objective basis to believe this, but it gives me pleasure to think it.

Aswath Damodaran (00:09:21):

And I believe we all get moments like that through our lives, but we’re so busy with our lives, we don’t listen. I tell my kids… They have social media, they’re constantly filling their days. And I still do this. Every day, I try to give myself some time. When I’ve nothing scheduled and I’ve open slots, it’s daydreaming time. I think we think about daydreaming as a waste of time. I think daydreaming is when you open your mind up to, “Hey, what can I do that’s different? What can I learn?” And I really value those moments because I think it makes a difference in my life…

…William Green (00:21:24):

But it also struck me that part of your skill was your willingness to provoke, to be a provocateur. And there was this wonderful beginning of the talk where, if I remember right, you said, “Basically, I sit at this nexus of these three really big, really badly run businesses of teaching, and writing, publishing, and finance. And they’re all begging to be disrupted and to be taken to the cleaners.” And I wondered if you had any advice for the rest of us on how to speak, how to communicate, because it seems to me that you’re really a master of this.

Aswath Damodaran (00:21:53):

I think that my two pieces of advice is don’t try to be somebody else. You got to be comfortable with your presence. And I’ll give you an example. I’ve never worn a suit to teach because when I started teaching, that was the standard. In business schools, people wore suits or [inaudible 00:22:09] ties when they walk to a classroom, because the view was students will not respect you if you’re not dressed up as if you’re an authority. And my view was, “Look, now if I bought a suit, I’m going to pay a few hundred dollars. My students are MBAs. They’re going to Barneys to get their suits for 3000 because they need to look good for investment banks. My suit is never going to look at as good as theirs and I hate wearing suits.” So I said, “Look, I don’t feel comfortable teaching in a suit. So, I’m going to teach in a T-shirt. I’ll teach in sweatshirts. Basically, I can teach in whatever makes me comfortable.” So, I had to pick something that made sense for me.

Aswath Damodaran (00:22:44):

Early on, I realized there’s some great teachers who were authoritarian teachers. I don’t know whether you remember the movie Paper Chase, I think where it’s about the Harvard Law School. And I don’t remember who it was, a great actor, maybe Gielgud was there playing the role. And he plays the role of a Professor of Law, and he intimidates. He has this immense presence in front of the classroom. But when he turns to a student, just the intimidation factor is enough to keep the class going. I realized very early that I was not in an intimidating person, that my presence couldn’t be built on, “I’m the authority figure, you’re not. And I’m going to tell you what to do.” So, I had to find a teaching style that fit me or a communication style that fit me. And my communication style is much more informal and much more open and much more willing to kind of accept the fact that there might be other people who push back. And over time, there are things I do better now than I did four years ago.

Aswath Damodaran (00:23:37):

One of the things I tell people is, “Look, there are days when you wake up and you get in front of a group, and you open your mouth and magical words come out. It’s like you can’t do anything wrong. You say, where did that come from?” It’s easy to teach when you’re in the zone, right? When baseball players are in the zone. When you’re in the zone, teaching is easy. Teaching or communication is difficult when you’re not in the zone. When you open your mouth and your tongue is getting in the way of your own words, it’s not your day. And I tell people, “You got to figure out ways to get into the zone when you’re not in the zone.” So, there are small tricks and I would suggest these. One is be well prepared. I’m prepared for my classes to the point I never have to look at my slides to know what’s on the slides.

Aswath Damodaran (00:24:18):

So I think that finding your zone when you’re not in the zone is something I do better now than when I started, because I’ve learned small tricks to bring myself back into the zone. Tricks like figuring out questions. One of the things you will notice in my slides is I’ve these questions asked or I give multiple choice answers and I put them up. So instead of throwing an open question to a group where nobody might react, I say, “Look, I’m going to throw this question up. I’m going to put five answers. None of the answers are going to be obviously wrong.” And I call for a minute of silence where people get to pick an answer. That minute actually helps me as much as it helps the students, because again, those moments allow you to gather your thoughts and say, “Okay, let me get back on track.” So, there are things I do now that keep me in the zone when I even…

Aswath Damodaran (00:25:03):[inaudible 00:25:00]. So there are things I do now that keep me in the zone even when I’m not feeling like I’m at my best. And being prepared, that I think is critical to teaching, but you’re right. One of the things I tell people is the biggest sin you can commit as a teacher is to bore people. I will provoke you. I will anger you. I’m willing to take any emotion over boredom. That doesn’t mean I’m going to prod at people just to make them mad. But it means that sometimes I would throw a question out that might be provocative because it challenges people’s beliefs.

Aswath Damodaran (00:25:32):

One of the first things I start my corporate finance class is I ask, “How many of you think markets are short-term?” Because that’s the conventional wisdom, at least is markets are short-term. We need to do other things to make them long-term. And about two-thirds of my class put up their hands and say, “Hey, I think markets are short-term.” And I say, “Can you give me a piece of evidence that backs up that view?” And it’s amazing how difficult it is to actually find actual evidence that markets are short-term.

Aswath Damodaran (00:25:57):

In fact, if you look at the actual evidence, you would conclude that markets are far too long-term. Otherwise, how can you explain the fact that you put $100 billion values on companies that haven’t figured out a business model yet? No short-term market would do that. So by opening up these questions where people have preset views and challenging those views, not because I want to change their views, that’s not my job, but to make them examine their own views. And if at the end they say, “I think markets there still short-term,” I’m perfectly okay with it. I’m not an evangelist when it comes to putting my views on others, but I want them to examine their own views…

…William Green (00:26:42):

One of the things I’ve particularly appreciated, and I’m agnostic about this. I don’t in any sense have the answer, but I really appreciate the way you’ve discussed ESG, the way you’ve been incredibly outspoken. This whole idea that companies should somehow be more environmentally and socially responsible and have better governance. And there’s obviously been a huge drive, commercially driven drive, I suspect from business leaders like Larry Fink, the CEO of BlackRock, to sell this idea to investors and to persuade everyone that it’s really beneficial for companies to do good, that it helps the bottom line and is profitable for shareholders.

William Green (00:27:14):

I think it’s fair to say that you are not convinced. And when I asked for questions on Twitter to ask you, there were several people who wrote to me about this. A listener named [Fabio Zugman 00:27:23], who I’m going to send a copy of my book, Richer Wiser Happier, to thank for his question, said to me, “You got to ask him about ESG.” And he said, “Do you think ESG will be a fad of the past? Or is it one of those things that will refuse to die as long as it serves as a marketing gimmick?” And so I wondered if you could talk us through this idea, why you’re so cynical about it, why you’re so skeptical.

Aswath Damodaran (00:27:44):

I first wrote about ESG in 2020, and I wrote about ESG because I’d never seen a concept explode that quickly out of nowhere to become the status quo. But usually concepts are the edges. No, the status quo had bought in, CEOs of companies. The corporate round table had bought this, signed the statement on stakeholders and how companies should be run for stakeholders. And the big investment funds led by BlackRock were pushing ESG to the forefront.

Aswath Damodaran (00:28:11):

But what made me suspicious was there seemed to be no trade-offs. So the sales pitch was you can have it all. You can do good and be more valuable. You can do good and earn higher returns. You can do good and you’ll have to sacrifice nothing. And through the history of humanity, being good has always been the more difficult choice. Being good has always cost you. In fact, if being good were the easier choice, we wouldn’t need religion in the first place, right? If the 10 commandments came to us as our natural choices, then why would we need religion?

Aswath Damodaran (00:28:41):

The nature of goodness is you got to have sacrifice. I’d have had a lot more respect for the ESG movement if they’d come up and said, “You know what, we need to make the world a better place. So companies have to accept that they will make less money and be less valuable in order to make the world a better place.” That investors have to accept lower returns because they want to be good.

Aswath Damodaran (00:29:01):

And if they’d made it a trade-off, I’d have said, “Okay, let’s talk. Let’s talk about what the trade-off is. Who’s making the trade-off? Who’s paying for this goodness?” And there’s still issues with ESG, but it would be an issue that you could talk about the trade-offs and say, “Does that make sense?”But the fact it was being sold as all good… It’s all cake, no calories. I said, “Somebody’s got to look under the hood.”

Aswath Damodaran (00:29:23):

So each of those in an area where I’ve seen this happen in the past, seen what happened. New concepts come up, which claim to be revolutionary, but really old wine in a new bottle claiming to be the magic way of coming up with a more valuable business. So it started with my favorite area, which is valuation. I said, “You guys keep telling me that ESG is good for value. Tell me where.”

Aswath Damodaran (00:29:46):

In my valuation class, I have a proposition called the It Proposition. If it does not affect the cash flows and it does not affect risk, let’s stop talking about it. So through time I’ve taken every buzzword in business and said, “Hey, whether it’s strategic considerations or China or cloud… Whatever that buzzword is, let’s talk about how it plays out in the cash flows and the risk because then we’re talking about something tangible.” Otherwise it just becomes this filler for whatever decision we want to make.

Aswath Damodaran (00:30:14):

So with ESG, that was my first reaction. Show me where. So I started looking at the evidence that ESG advocates were presenting. And I was horrified by the quality of research that passes for ESG research. Because, to be quite honest, it seemed to me that the research had many problems. One was, it was written by advocates, true believers. And they might have been deluding themselves saying, “I’m an objective researcher,” but when you start with a presumption or a prior that’s too wrong, it’s almost impossible to do clean research.

Aswath Damodaran (00:30:45):

The second was, they weren’t even sure what question they were answering. They were mixing up whether it was good for companies and whether it was good for investors in the same research. And the reason is very simple. One of the stories that has some backing to it is that ESG can make companies safer by protecting them from doing something stupid that can create a crisis.

Aswath Damodaran (00:31:05):

And I’m willing to listen to it. But if that story is true and ESG makes companies safer, those companies should have lower [inaudible 00:31:13], lower cost of equity, lower cost of capital. That’s good. But that means in the investors in those companies should earn lower returns as well. So what’s good for companies then can’t be good for investors as well. And much of this research was mixing up what was good for companies, what was good for… They weren’t sure what the question they were answering was.

Aswath Damodaran (00:31:31):

When I first started, very few people were pushing back. In the two years since, of course, the pushback has become much more tangible. And to be quite honest, I wrote a piece about ESG yesterday that I posted on my blog. I’m done with ESG, and I don’t want to re-fight. I’m going to move on to something else because I’m a dabbler. My interest has run out and I’ve pretty much said what’s on my mind. I’ve told people where I’m coming from and why I think what I do. I’ve no interest in forcing my thoughts on other people. And I will put out my views and if other people take strands of it and push back or make it their views, I’m completely okay with it. But I just wanted to make sure that people understood where I was coming from…

…William Green (01:01:23):

I was very struck by a wonderful line of yours that I think may have come from that Numbers and Stories book, which is a terrific book actually, where you wrote, “Humility as the single most important quality, you need to be a successful investor.” You also said hubris lies at the root of so much investing pain. Can you talk a bit more about how to guard against our own hubris and overconfidence? Because this is something that, particularly, for highly intelligent people who are used to being right and getting good marks at school and then they become investors, it’s an incredibly seductive mistake to make to assume that you’re going to be right in this game where you’re competing with other people who are equally brilliant and equally well qualified.

William Green (01:01:59):

So can you talk about that challenge of just dealing with overconfidence and hubris?

Aswath Damodaran (01:02:06):

The Buddhist are very fond of the word serene and the essence of serenity is when good things happen to you, don’t get over exuberant about what happened, and when bad things happened to you, don’t get down in the dumps, and investing is a lot of ups and downs. There are days you wake up and say, “That was an amazing day. My portfolio was up 8%.” Next day, you wake up and the end of the world is come, and recognizing that so much of what happens in markets has nothing to do with your great analysis or skill. It’s got to do with luck.

Aswath Damodaran (01:02:36):

This is a game where luck is the dominant paradigm, and it’s not like I tell people the difference between basketball and investing is you and I can go out there and try to shoot three pointers. Once in a while with luck, you might get one out of every 50, and I don’t even think I could get that, and as Steph Curry goes and do it, he does it 30 out of 50. Clearly, luck is not what’s explaining it. It’s skill. In investing though, you could get 30 hits in a row, and I can’t reject the hypothesis that he just got lucky 30 times in a row. It’s so difficult to separate.

Aswath Damodaran (01:03:08):

One of my favorite books, and I don’t know whether you’ve had Michael Mauboussin on your row, but you should definitely have him. He’s-

William Green (01:03:14):

He’s great.

Aswath Damodaran (01:03:14):

Separating out luck from skill in investing is how difficult it is to do, and that’s where humility comes from. It’s recognizing when you’re successful, how much of your success comes from luck. I still get asked by people, “What do you make around the market?” Usually, I don’t go around talking about my past performance because if I’m not asking for your money, really, it’s none of your business, whether I beat the market or not. But if I added up the returns, maybe they’re just curious. I might have made 3% or 4% more than the market going back over the last 30 years.

Aswath Damodaran (01:03:42):

Then they ask me, “Well, that must be payoff for you.” I say, “I have no idea what it is. I just might have gotten incredibly lucky at the right times.” I tell them about some of my successful investments. When I bought Apple in 1999, I bought it because I sorry for the company. Actually, I bought it as my charitable contribution. I’ve been an Apple user since 1981. Remember, ’99, Apple was facing a near-death experience. Their computers were not selling. It was just as Steve jobs was coming back, and they didn’t seem to be any way that you could recover from this crash.

Aswath Damodaran (01:04:12):

I bought Apple because I was I said, “You know what? They’ve been good to me, and I’m going to spend $5,000 buying Apple shares that I can write off.” Best investment I ever made, turned out to be a investment I made because I was feeling sorry for a company. The hubris, in my part, to go around starting with my return saying, “Look how great my investment in Apple was. “Without telling you that investment had nothing to do with doing full-fledge intrinsic valuation, and some are jumping in at exactly the right time. So it’s hard work though.

Aswath Damodaran (01:04:39):

I mean, it’s easy to let things go to your head, and the market, it’s just waiting for that to happen. It’s almost like markets are waiting and hiding for you to get all caught up in how good you are. So when I see these shooting stars the people who are lauded as the great investors because they’ve done well for two or three years, I say, “You know what? Just give it some time, because most of the time when you succeed, it goes to your head.

3. There’s a Piece of EV Tech Where the U.S. Has an Edge on China – Stephen Nellis and Gregg Lowe

hina dominates the electric vehicle supply chain, from processing raw minerals like lithium into chemicals for batteries all the way to building finished cars. But there’s one niche where America still has an edge: chips made from an exotic material called silicon carbide.

In EVs, these chips are used in inverters, which sit between the car’s battery and motors, converting the direct-current electricity the battery supplies into the alternating current the motors require. Such chips always lose some energy as heat, but silicon carbide chips lose far less than those made of conventional silicon. That difference can boost the range of an electric car 5% to 15%.

But the raw material for silicon carbide chips is difficult to manufacture. North Carolina–based Wolfspeed supplies about three-quarters of the world’s silicon carbide wafers—the thin discs on which chips are made, according to Piper Sandler analyst Harsh Kumar. Wolfspeed sells the wafers to major automotive chip firms including STMicroelectronics, Infineon and Onsemi, but also makes finished chips itself. In the coming weeks, Wolfspeed will open a $1 billion factory in upstate New York to boost its efforts to compete directly with those customers in making and selling the finished chips…

…Why should anybody care about something as esoteric as silicon carbide?

In a combustion engine car, think of your fuel lines going from your gas tank to your engine. With a silicon chip, it’s as if someone has poked a hole in it. As your fuel is coming to the engine, you’re just dumping a bunch out on the street. Your miles per gallon are going to be less because you’re losing some gallons as you’re driving. That doesn’t happen with silicon carbide.

​​This is a big deal for two reasons. One, the range of the car is longer, which is an important metric for people buying electric cars. Two, the amount of battery you need to drive a certain distance is less, and batteries are the most expensive thing in an electric car. So if you use fewer batteries, the car is going to be cheaper, which is another thing people care about…

…You’ve got a supply agreement with General Motors. Why are companies like GM coming directly to you?

The car companies have realized that they need to better understand their supply chains of semiconductors, and they need to get closer to the semiconductor manufacturers. I’ve been in this industry for 35 years, and never have I seen so many car factories being shut down because you can’t get a chip. So there’s been a wake-up call.

There’s a second element that is really important. The engine of a vehicle is the personality of the car. Some companies name their cars after the engine. For BMWs, the last two digits in the model name are the displacement of the engine in liters. A 525 is 2.5 liters, and a 550 is 5.5 liters, and so on. As technology goes from internal combustion engines to electric, the carmakers are trying to get their heads around it: How do we create our personality in this new engine, this inverter and the motor associated with it?…

…How are you thinking about China as a competitor? Are Chinese chipmakers also racing to develop silicon carbide manufacturing technology? And if so, how close are they to you?

They are, and so are our customers like the Infineons of the world. But this is a technology that’s really difficult to come after. Silicon carbide grows in a machine that operates at 2,500 degrees Celsius. That’s almost half the temperature of the sun. So this is not for the faint of heart.

You can’t buy that equipment on the open market. There’s not a vendor of silicon carbide machines. So that means you need to build it yourself. Well, to know how to build a machine like this, you need to know how to make silicon carbide. But to make silicon carbide, you need to know how to build a machine like this. There’s this whole startup process that takes many, many years. Our startup process began 35 years ago when the company was founded. And what we use today is dramatically different from what was used 35 years ago.

The game plan for typical Chinese companies is to take a bunch of capital and throw it at a problem. They can’t do that because you can’t just buy these machines. So I think that’s going to be a bit of a challenge. But the world’s supply of people that really understand this technology is pretty small.

We always have a healthy bit of paranoia around this. But it’s really tough.

4. TIP440: Beating The S&P500 Since 2004 w/ Bryan Lawrence – Stig Brodersen and Bryan Lawrence

Bryan Lawrence (00:17:22):

The second reason durable cash flows are great is that durable cash flows are more predictable. And the predictability of cash flows is a big advantage to a stock picker because they make valuing those cash flows more certain. And having certainty about valuation is a big advantage given how volatile share prices are, how volatile are share prices? This has amazed me since I started the business. When I started Oakcliff in 2004, I was lucky enough to find myself in a room with Warren Buffett and two dozen other aspiring stock pickers. We were very happy to ask him lots of questions, which pretty much all boiled down to, “How do we get to be like you but faster.” He very nicely broke to us the bad news that stock picking was a long game, but he said, “I do have a piece of good news for you, the average stock goes up and down by 80% in a year. And that’s an enormous advantage if you actually take the time to understand the underlying business because the stock price is not reflecting underlying value if it’s going up and down by 80%.”

Bryan Lawrence (00:18:17):

I said to myself, “80% in a year, he’s got to be out of his mind. He’s Warren Buffett, but he’s lost his mind.” I went back to New York, and I did the calculations he was suggesting, which was to compare the 52-week high to the 52-week low for every stock in the stock market and compare the percentage difference between those two things. And when I did the calculations, maybe not surprising because he is the Sage of Omaha, he was right. You can use Bloomberg and a computer to crunch these numbers for the thousands of companies. It’s about 4,000 companies in the US stock market going back 20 years. And if you do it, we do it about once a year, the answer is as astonishing now as it was in 2004 when I started.

Bryan Lawrence (00:18:55):

During a calm year like 2019, the average US stock price goes up and down by 50%, 5-0%. And in a crisis year, like the dot-com crash, we had in 2000 or the 08/09 financial crisis or the pandemic we just had in 2020, by up to 200%. Buffett, by saying 80%, was basically averaging a calm and a crisis year. That 50% in a calm year is also a median, and in a median year where it’s 50%, you have many stocks that are bouncing up and down by 80%. There’s no way that the intrinsic value of the average business is going up and down by so much each year, and this is a big advantage for a stock picker who’s done the work…

…Stig Brodersen (00:25:52):

Oakcliff’s net return to clients has underperformed the S&P 500 at eight out of 18 years, and yet your returns to clients outperformed the S&P 500 over time. I just wanted to mention some of those numbers. I also said it in the introduction before we kicked off this interview, Bryan, but I just can’t help but mention it because you’re too polite for you to say it yourself. But the S&P 500 with exception of Oakcliff Capital was 494.2% for the S&P 500, and net of fees is 718.3%. So, I mean, this is just an amazing track record. So bravo. You managed that impressive track record and at the same time, you underperformed the S&P 500 eight out of 18 years. I’m curious to hear your thoughts on that.

Bryan Lawrence (00:26:37):

Well, thank you, Stig. But we have had periods of underperformance, and those periods of underperformance have lasted for a year or more. This is not surprising. Warren Buffett gave a speech in 1984 about the super investors of Graham-and-Doddsville, which I would encourage your listeners to go find on the internet if they haven’t already. Just Google super investors of Graham-and-Doddsville and read Buffet’s speech and then the response by a professor at Columbia Business School, where he gave the speech. There are a couple of really interesting conclusions that can be drawn from that speech, basically, every concentrated value investor will underperform the market on an annual basis 30 to 40% of the time. It jumps out of the data. And this is data as of 1984, but you can carry this data forward and you’ll find it to be true.

Bryan Lawrence (00:27:29):

I think it’s an iron rule of underperformance. Joel Greenblatt talks about it. Warren Buffett talks about it. Here’s some data which is just fascinating. If you look at Berkshire Hathaway itself, okay, which is run by the patron saint himself, Warren Buffett, Warren has controlled Berkshire Hathaway for 57 years now, going back to 1965, and Berkshire Hathaway has underperformed the S&P 18 of those 57 years or 32% of the time. There’s that iron rule, 30 to 40%. You could say, “Oh, is that a function of the fact that he’s managing more and more money, making it more and more difficult for himself?” The answer would be no, because if you look at the first 25 years that he controlled Berkshire Hathaway, 1965 to 1990, he underperformed nine of those 25 years or 36% of the time.

Bryan Lawrence (00:28:21):

I think this is a reason why concentrated value investing, while it delivers great long-term results if it’s being done by people who actually have the ability and the temperament to handle it, why a lot of people kind of lose faith with it because you will find every practitioner of it having these periods of underperformance.

5. Quantum computing has a hype problem – Sankar Das Sarma

I am as pro-quantum-computing as one can be: I’ve published more than 100 technical papers on the subject, and many of my PhD students and postdoctoral fellows are now well-known quantum computing practitioners all over the world. But I’m disturbed by some of the quantum computing hype I see these days, particularly when it comes to claims about how it will be commercialized.

Established applications for quantum computers do exist. The best known is Peter Shor’s 1994 theoretical demonstration that a quantum computer can solve the hard problem of finding the prime factors of large numbers exponentially faster than all classical schemes. Prime factorization is at the heart of breaking the universally used RSA-based cryptography, so Shor’s factorization scheme immediately attracted the attention of national governments everywhere, leading to considerable quantum-computing research funding.

The only problem? Actually making a quantum computer that could do it. That depends on implementing an idea pioneered by Shor and others called quantum-error correction, a process to compensate for the fact that quantum states disappear quickly because of environmental noise (a phenomenon called “decoherence”). In 1994, scientists thought that such error correction would be easy because physics allows it. But in practice, it is extremely difficult.

The most advanced quantum computers today have dozens of decohering (or “noisy”) physical qubits. Building a quantum computer that could crack RSA codes out of such components would require many millions if not billions of qubits. Only tens of thousands of these would be used for computation—so-called logical qubits; the rest would be needed for error correction, compensating for decoherence.

The qubit systems we have today are a tremendous scientific achievement, but they take us no closer to having a quantum computer that can solve a problem that anybody cares about. It is akin to trying to make today’s best smartphones using vacuum tubes from the early 1900s. You can put 100 tubes together and establish the principle that if you could somehow get 10 billion of them to work together in a coherent, seamless manner, you could achieve all kinds of miracles. What, however, is missing is the breakthrough of integrated circuits and CPUs leading to smartphones—it took 60 years of very difficult engineering to go from the invention of transistors to the smartphone with no new physics involved in the process.

6. 103 Bits of Advice I Wish I Had Known – Kevin Kelly

• About 99% of the time, the right time is right now.
• No one is as impressed with your possessions as you are.
• Dont ever work for someone you dont want to become…
• …Ask funders for money, and they’ll give you advice; but ask for advice and they’ll give you money.
• Productivity is often a distraction. Don’t aim for better ways to get through your tasks as quickly as possible, rather aim for better tasks that you never want to stop doing.
• Immediately pay what you owe to vendors, workers, contractors. They will go out of their way to work with you first next time..
• …Speak confidently as if you are right, but listen carefully as if you are wrong…
• …The best way to get a correct answer on the internet is to post an obviously wrong answer and wait for someone to correct you. You’ll get 10x better results by elevating good behavior rather than punishing bad behavior, especially in children and animals…
• …Don’t wait for the storm to pass; dance in the rain…
• …When you have some success, the feeling of being an imposter can be real. Who am I fooling? But when you create things that only you — with your unique talents and experience — can do, then you are absolutely not an imposter. You are the ordained. It is your duty to work on things that only you can do….
• …Your best job will be one that you were unqualified for because it stretches you. In fact only apply to jobs you are unqualified for…
• …A wise man said, “Before you speak, let your words pass through three gates. At the first gate, ask yourself, “Is it true?” At the second gate ask, “Is it necessary?” At the third gate ask, “Is it kind?”…
• …. Getting cheated occasionally is the small price for trusting the best of everyone, because when you trust the best in others, they generally treat you best…
• …You see only 2% of another person, and they see only 2% of you. Attune yourselves to the hidden 98%.
• Your time and space are limited. Remove, give away, throw out things in your life that dont spark joy any longer in order to make room for those that do.
• Our descendants will achieve things that will amaze us, yet a portion of what they will create could have been made with today’s materials and tools if we had had the imagination. Think bigger.
• For a great payoff be especially curious about the things you are not interested in.
• Focus on directions rather than destinations. Who knows their destiny? But maintain the right direction and you’ll arrive at where you want to go.
• Every breakthrough is at first laughable and ridiculous. In fact if it did not start out laughable and ridiculous, it is not a breakthrough.

7. The Rich And The Wealthy – Morgan Housel

Cornelius Vanderbilt left his heirs the inflation-adjusted equivalent of something like $300 billion. Within 50 years it was gone.

In between sat three generations whose primary purpose was to compete on who could build the largest house and marry the bluest blood. The first heirs had some entrepreneurial sense of running the family business; over time the “family business” became insecurity and resentment.

In 1875 an op-ed said socialites “devote themselves to pleasure regardless of expense.” A Vanderbilt responded that actually they “devote themselves to expense regardless of pleasure.” It was a game that couldn’t be won, so everyone lost.

Reggie was one of the last Vanderbilts to inherit significant wealth. On his 21st birthday he received $12.5 million, or about $350 million in today’s dollars…

…Reggie’s two loves were brandy and gambling. The first left him dead at age 45, with cirrhosis so severe the blood flow from his liver was cut off and pushed up to his esophagus, where the veins abruptly ruptured and left him choking in a pool of blood. The latter left him broke – after repaying debts Reggie’s will was nearly irrelevant, as he had nowhere near the amount of money promised to his heirs.

Reggie’s grandson – Anderson Cooper – was one of the first Vanderbilts who was never promised dynastic wealth. It may have been a blessing. Cooper once said of inheritance: “I think it’s an initiative sucker. I think it’s a curse. From the time I was growing up, if I felt like there was some pot of gold waiting for me, I don’t know if I would have been so motivated.” It’s like he was the first Vanderbilt to be set free…

…I’m always interested in the difference between getting rich and staying rich. They are completely different things, and many of those skilled at the former fail at the latter.

Part of this topic is knowing the difference between rich and wealthy.

These definitions are my own, but here’s the distinction: Rich means you have cash to buy stuff. Wealth means you have unspent savings and investments that provide some level of intangible and lasting pleasure – independence, autonomy, controlling your time, and doing what you want to do, when you want to do it, with whom you want to do it with, for as long as you want to do it for.

What I find fascinating are stories like the Vanderbilts, who were the richest people on earth but, by my definition, some of the least wealthy. Money to them was less of an asset and more of a social liability, indebting them to a status-chasing life that left most of them seemingly miserable.

George Vanderbilt spent six years building the 135,000-square-foot Biltmore house – with 40 master bedrooms and a full-time staff of nearly 400 – but allegedly spent little time there because it was “utterly unaddressed to any possible arrangement of life.” The house nevertheless cost so much to maintain it nearly ruined Vanderbilt. Ninety percent of the land was sold off to pay tax debts, and the house was turned into a tourist attraction.

There are so many similar stories from the Vanderbilt family that you begin to ask, “What was the point?”

The point, as the New York Daily Tribune realized early on, was not to live a great life. It was to be rich – to be valued “upon no better basis than the possession of money.” Rather than using money to build a life, their life was built around money; rather than an asset, their inheritance was an insurmountable lifestyle debt, passed to the next generation until there was mercifully nothing left.

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Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. Of all the companies mentioned, we currently have a vested interest in Apple and Salesforce. Holdings are subject to change at any time.

We’ve constantly been sharing a list of our recent reads in our weekly emails for The Good Investors.

Do subscribe for our weekly updates through the orange box in the blog (it’s on the side if you’re using a computer, and all the way at the bottom if you’re using mobile) – it’s free!

But since our readership-audience for The Good Investors is wider than our subscriber base, we think sharing the reading list regularly on the blog itself can benefit even more people. The articles we share touch on a wide range of topics, including investing, business, and the world in general.

Here are the articles for the week ending 24 April 2022:

1. Axie Infinity’s Financial Mess Started Long Before Its $600 Million Hack – Adi Roberston

Axie Infinity — whose creators refer to it as both a “nation” and “a bleeding-edge game that’s incorporating unfinished, risky, and highly experimental technology” — is sort of like hyper-financialized Pokémon. Players buy or rent three non-fungible tokens (or NFTs) linked to cartoon axolotls called “axies,” each of which has a set of associated stats and battle cards. Winning battles grants players a token called a “smooth love potion” or SLP, and axies can be “bred” with SLP and a third token called AXS to produce new NFTs.

Axie’s biggest selling point is the chance to turn these tokens into real money. Axies and SLP can be sold for cryptocurrency, and people can earn SLP by either playing the game directly or participating in the “scholarship” system, where they lend their axies to other players and receive a share of those players’ earnings. The result is a kind of tremendously popular in-game capital market, where axie-holders can earn currency through investment without necessarily playing the game.

The dream of Axie Infinity, like a lot of blockchain applications, is to get paid for something you currently do for free online. As Andreessen Horowitz partner Arianna Simpson told my colleague Casey Newton last October, “If I can play a game, and have an equivalent amount of fun, and also make money — well obviously I’d rather do that, right?” (We’ll leave aside the philosophical questions this raises about the nature of fun.)

But there is a fundamental problem: Axie Infinity’s in-game economy has so far relied on constant growth to keep it running, with inflation built into the mechanics. Even if the game can overcome the recent challenge of the hack, Sky Mavis hasn’t proven it can transition out of that phase.

Axie Infinity’s economy is built around three major resources: the in-game cryptocurrency SLP; the axies that live as in-game items as well as NFTs on Sky Mavis’ blockchain; and the “governance token” AXS. The game produces two of those resources in constantly increasing quantities. SLP is earned through player-versus-player battles, and, until recently it was also available by completing daily quests and grinding in single-player mode, the equivalent of printing money and handing it to players in large quantities. Axies can be bred several times to produce new creatures and are largely immortal, so the breeding mechanic increases the pool of NFTs.

Games often include economic “sinks” (like cosmetic items or in-game equipment maintenance costs) that burn resources without producing more. By contrast, Axie Infinity players had two main options: they could sell their SLP — which pumped it back into the ecosystem — or use it to breed axies whose main function is producing even more SLP. Either way, they were creating more resources and watering down the value of everything acquired in the game.

“From a macro[economic] perspective, you’ve created a positive feedback loop,” explains Mihai Gheza, the cofounder and CEO of Machinations, a consultancy that tests game economies with large-scale software simulations. Players (especially scholars) would use axies to produce SLP, the SLP would produce more axies, and the axies would bring even more SLP-producing players into the game. “It’s a guaranteed means of creating inflation.”

Sky Mavis said it needed a growing axie pool to let new players join Axie Infinity because, unlike a traditional game, the studio wasn’t supposed to simply create more characters out of thin air. Eventually it planned to introduce more sinks and hoped people would acquire axies for “the intrinsic value they can provide to players in the form of competitive, social, and progression-based fun and achievements.” In the short term, their primary use was generating currency that could create more NFTs for sale or rental, and that only worked if there were people around to buy. “By design the Axie economy will be dependent on new entrants,” Sky Mavis acknowledged.

But unless that intrinsic value materializes, the system requires players to keep joining up. In August, a cryptocurrency writer and decentralized autonomous organization operator who goes by M Goes wrote a widely cited Medium post calling Axie Infinity “the biggest Ponzi scheme in crypto.” He concluded that none of Axie Infinity’s potential long-term business models could support its biggest short-term selling point: letting a large number of people make a consistent full-time living playing games. The system was only sustainable with a huge demand for more SLP and axies, and maintaining that would require a functionally infinite number of new signups. “It is hard to predict when the collapse will happen,” he wrote. “But nevertheless, there are only so many daily players it can reach.”

As it turned out, Axie Infinity skeptics wouldn’t have to wait long. Around the end of 2021, the game suffered a dramatic decline in its token prices and sales volumes, with the SLP token crashing from an all-time high of 39 cents to a single penny. A report from research firm Naavik indicated that the typical player’s daily earnings had fallen below the minimum wage in the Philippines, Axie Infinity’s top market. Sky Mavis took drastic action, removing a large chunk of SLP-generating options and making player earnings dependent on winning competitive matches instead of just showing up to grind. “We know that this is painful medicine. The Axie economy requires drastic and decisive action now or we risk total and permanent economic collapse,” it warned. “That would be far more painful.”

2. Things not being said about Chinese tech management – Lillian Li

When Alibaba, Tencent and Baidu started in the 2000s, there was no concept of tech entrepreneurs. People have always started small businesses, but no one in living memory ever built a private business empire in China. VCs were mistaken for fraudsters — in fact anyone starting a business was mistaken for fraudsters. For a country undergoing the initial tremblings of liberalisation and digitalisation, two groups went to work for fledgling domestic startups— the crazy innovative self-starters and the people who couldn’t get a better job in either SOEs or MNCs. That’s a big gap in competence between the two.

This meant while the tech founders were impressive people, some of the early employees of these corporations were decidedly not. Talking to an early Tencent VP, he mentioned his co-workers did not have prestigious university degrees if they had university degrees. Before listing, the average coder in Tencent graduated from the Chinese equivalent of community colleges and was very average. This was not a localised phenomenon by any means. Some people get lucky by being at the right place at the right time. Their positions are more luck than merit. While this is also the unspoken rule in Silicon Valley much of the time, the difference is stark in China, given the heterogeneous distribution of education and the assumed inherent worth that accompanies education.

The early employees also tend to be missionaries relative to the mercenaries of the later cohort.2 Someone who joined Alibaba in 2012 joined an upstart on the cusp of changing the Chinese retail landscape. Someone who joins Alibaba in 2022 is entering an establishment potentially on the decline. The graduates who join tech firms are the best talent of their generation, but they join for the money and prestige more than the love of the mission. The intergenerational gap is stark.

Implications from these factors are numerous. First, there is a generational disconnect where older employees believe in the notion that tireless hard work yields rewards. After all, they experienced this with vested stock growth. The younger generation is there for a job, not a purpose. They want to know when they can afford a house. Second, early mediocre employees who made it to middle management oversee more qualified and talented underlings. People do not scale with organisations, but growth hides many ills. Insecurity abounds when managers, alongside their employees, realise that they aren’t as qualified to be holding the positions they do…

…Management and organisation excellence was a luxury for companies with stable growth and a longitudinal timeline. It was possible to brute force solve a problem with additional bodies in the early days. Hiring more people is still the default modus operandi of many firms when they encounter operational bottlenecks on tight deadlines (and deadlines are always tight, there are no prizes for being slow to a market). It also stroked the leader’s ego to be overseeing many people. After all, being in charge of such resources was a direct approximation of power.3

The very distinct problem of organisational bloat and diseconomy of scale with hiring people is apparent here. Instead of having 1:1s with their direct report, management tells employees to write daily, weekly, monthly, and quarterly reports listing what they’ve been doing. Not to mention the inefficiencies caused by hiring – it takes time to get new employees up and running, often dragging down the productivity of others during the ramp-up. Communication and coordination get harder as group size increases. The inability to attribute direct outcomes to individuals creates visible principal and agent problems. Entire work culture arises where employees slack off (touching fish culture) and management makes countermoves without addressing the real issues — firms overhired during product sprints then have to deal with excessive headcounts. 

The focus is for firms to get things done quickly, and the attitude is whatever that takes. Refinement of process and improving efficiency generally took a back seat. This approach afforded fluidity and agility. Work calls happen all day, every day. Project directions can change on a dime, and the teams will reorient. Less time is spent on strategy and more on execution and reiteration. Communication takes place over the fragmented synchronous WeChat more than email or work messaging platforms like DingTalk or Feishu. Calendar invites are getting wider adoption, meeting agenda-less so.

There seems to be a cultural background to the lack of optimisation. While Western firms credit their success to distilling and adopting industry best practices, Chinese firms credit their success to being one of a kind. Chinese management exceptionalism takes Western startup slogans like ‘move fast and break things’ and mixes them with the local customs of patronage linked to Jianghu culture. The assumption is that every firm’s process should be unique, and there is some resistance to change. This has been stalling the adoption of successful organisational processes like sales funnel across China, yet another reason why Chinese SaaS finds it hard to take off. 

3. Christopher Tsai, is investing an art? Insight of a good investor – Peridot Capital Management

[00:07:04] Tilman Versch: Maybe this is a question that’s quite broad. For your 25-year-old self, what knowledge and strength do you feel that looking back, you’ve missed as a 25-year-old and you had to acquire maybe also in a bit painful way over the years.

[00:07:23] Christopher Tsai: You asked me about curiosity before. I think that’s also at the root of this question. Being able to constantly think about the world in different ways and not get trapped using models that you might have used or other people use is so important. It’s not a lesson that you can just teach. It’s an experience that one has to go through.

I’ve been reading this book. I’m not finished with it, because it’s a long book. It’s Marcel Proust’s In Search of Lost Time. Proust says that the real voyage of discovery is not seeking new landscapes but having new eyes. There lies the curiosity that we’ve been speaking about. It’s important to look at the world constantly with new eyes, particularly because the world is changing very quickly, right? Businesses don’t have the lifespan that they used to have.

In 1958, McKinsey did this study. And McKinsey showed back then that the average lifespan of a company was 61 years. That’s incredible. Sixty-one years, six decades. But today, that number is 18 years. And one of the reasons, Tilman, that it’s 18 years is because technology is encroaching upon old business models. So, if you’re not thinking about the world in new ways, if you’re not curious, if you’re not constantly looking at the competitive threat that technologies posed to traditional businesses, you might find yourself in a business that’s going out of business.

So again, being curious is not something you can just teach. It’s something that you, I think, have, and it’s something that you can foster over time. Eleanor Roosevelt, by the way, said something really wonderful. She said, “I think at a child’s birth, if a mother could ask a fairy godmother to endow it with the most useful gift, that gift would be curiosity. I wish I was endowed with that gift and was able to foster it from the very beginning. I think that something in business that you learn, you either have it or not that curiosity. But I love looking at different businesses, different business models, especially today.

[00:10:29] Tilman Versch: You have close to 25 years of experience in managing your fund. Which topics have you worked on since these 25 years to get better at? Are there any consistent topics that have kept you up at night? Let’s say it makes you stay late because you’re still trying to achieve and get better with?

[00:11:00] Christopher Tsai: Let me draw a parallel to answer your question between investment management and the Michelin Guide. We know the Michelin Guide for restaurants. Chefs work their whole lives to get one star and then two stars and three stars. What gets them there? Well, creativity gets them there, pushing the boundaries and being the best at what they do. They’re not doing things like everybody else, by definition. There are only a handful of chefs in the world that have three Michelin stars. The problem is that for those few chefs that wind up getting those three stars, what do they then want? Well, typically, they want to maintain those three stars. And so, everything else becomes subordinate to keeping those three stars.

I think that investment management is, unfortunately, very similar to that. And so, when I started, I was inundated with the idea of structuring a portfolio in a way that would get you those three stars, if you will. So that meant looking at beta, looking at Sharpe ratios, looking at standard deviation. And what I found over time is that if you start to behave like everybody else, your performance is going to be like everybody else at best. So over time, I have refined our process. In fact, we moved away from trying to worry what other people thought about how the portfolios looked. We moved away from that a long, long time ago. Maybe three-four years into managing capital for outsiders.

So today, it’s all about structuring the portfolio in the most optimal way. What do I mean about that? I mean, it’s about structuring a portfolio to maximize return and minimize risk. And that’s pretty much all I think about in terms of managing the portfolio—maximize return, minimize risk. I don’t worry about so many items that institutional investors worry about that wind up restricting a manager’s ability to have the flexibility and create alpha. I don’t worry about what other people might think of the portfolio. The key is to manage portfolios as if nobody was looking. So that’s how I’ve moved things over time…

…[00:24:55] Tilman Versch: Are there lessons you’ve taken directly from your grandmother.

[00:24:58] Christopher Tsai: She had a saying, “Don’t be a square table when you can be around one.” She intuitively understood Dale Carnegie.

[00:25:08] Tilman Versch: This means?

[00:25:10] Christopher Tsai: It means that there’s no need to be abrasive in how you speak with other people. I think that Fred Rogers, I’m not sure if many of your viewers know who Fred Rogers is, but he was the character Mr. Rogers, a popular TV show in the states geared toward children. And he said, “There are three ways to ultimate success. To be kind, be kind, be kind.”

Everybody is going through the same kind of emotions. Everybody has difficulties. You don’t know what those difficulties are. Everybody has a bad day from time to time. Everybody has joys, desires, needs, wants to be loved. My grandmother understood that. She knew how to deal with people. Not to be abrasive, not to be square around the edges. Dale Carnegie espouses that way of behaving. And so did Fred Rogers, who was one of my mentors. I should say idols…

…[1:09:28] Tilman Versch: For the end of our interview, is there anything you would like to add that we haven’t discussed?

[1:09:34] Christopher Tsai: Was it Mark Twain that said, “It ain’t what you don’t know that gets you into trouble, it’s what you know for sure that just ain’t so.” I think we should all keep Mark Twain in our mind as we think about what we own, why we own it. And again, always remain curious, not judgmental. Try to understand that if something doesn’t make sense to us, or something doesn’t make sense on the surface, maybe it makes perfect sense, just not to us.

I’ll give you an example. If you go back to 2007, Apple had just launched its iPhone. Many investors at the time said, “Well, the market value of Apple makes no sense.” They said it didn’t make sense because Apple was worth more than Nokia, Palm, Research in Motion, all those companies combined. How can that be? And what people didn’t understand, or at least a lot of people didn’t understand, was that Apple was shifting to an entirely new business model. And it had a product that had tremendous network effects that were not understood. And the market didn’t understand that the other companies would actually be completely disrupted by this new business model, by this new product, by new technologies converging. So, the market as a whole got that.

And that’s why Apple was worth more than the competitors combined. But the short sellers didn’t get that. They were close-minded. They were looking at the world through a lens or with models that no longer made sense. They didn’t understand the changes. But if you think about it, if a company has a positive future, a bright future and its competitors don’t, the value of the company that is leading disruption in taking market share and growing profits will not just be worth one multiple of all its competitors combined, but as time goes on, it will be worth two times, five times, ten times, 100 times and ultimately an infinite number of times, as all the other businesses continue to lose cash flow, and the present value of those future cash flows decreased, and the intrinsic value of those businesses decrease.

We’re seeing that same argument today in certain areas, where the value of one company might be worth all the competitors combined. People are making the same mistake because ultimately, that one company boot will be worth two times, five times, and ten times and an infinite number of times of all the other companies combined that may no longer have any cash flow. It’s just mathematics. It’s a numerator over the denominator. But it’s catchy, right? It’s a very catchy thing when somebody says, “Heck, this makes no sense. This company is worth all the combined value of the other players.” It’s very catchy. And it’s powerful, for some reason. I haven’t figured out why that’s such a powerful argument, but it’s very powerful. We all have some cognitive bias there, or at least I have. It’s a powerful argument.

But if you actually break it down and you figure out, “Okay. What does that mean? What is the value of a company? How’s the math thing compared? Numerator over denominator. What’s happening to the denominators? What’s going down? What’s happening to the numerator? Well, it’s going up because the future cash flows are increasing. Right? The present value of those cash flows is increasing, and intrinsic value is increasing. So obviously, it becomes multiples, not just one or two times. So be curious, not judgmental, as Walt Whitman said, and always look at the world with new eyes.

4. TIP429: What Is Happening With Oil? w/ Josh Young – Trey Lockerbie and Josh Young

Trey Lockerbie (00:04:06):

One chart I noticed from your research shows global production and consumption rising together in this highly correlated fashion, basically since 2010. 2020 hits, and both declined dramatically from the pandemic, but similar to the stock market, they’re beginning to kind of bounce back. However, the chart indicates that the supply will severely lag demand moving forward. And I thought this was kind of interesting because I was curious as to why the supply wouldn’t bounce back just the same to where it was kind of in 2019 levels?

Josh Young (00:04:38):

I think there’s two different cycles that are happening simultaneously for oil. And I think that’s where a lot of the headlines have been kind of in reporters covering the space have been confused, along with a lot of the analysts that cover it. So there’s a long cycle, which is that oil has been in a bear market since roughly 2012. And really, oil never achieved the high price that was seen in 2008. And so arguably it’s been in a prolonged bear market, even since let’s say 2008. Then there was a shorter cycle boom and bust in shale investment that was primarily spurred by private capital, by endowments and pensions and whatever allocating to private equity funds and equity and debt, where they went and drilled shale which was a particular kind of oil field that has a very high initial production rate and very high decline and has been most economic here in the US.

Josh Young (00:05:29):

There was this mini cycle for oil shale here in the middle of this down cycle for oil. And so what you had happen was a lot of long cycle projects that take a while, but aren’t really low decline. They produce for a number of years without a lot of necessary reinvestment. And you had a prolonged and extended down cycle for conventional development for oil, partly because there was this shale boom and bust. And the boom and bust for shale has been heavily politicized. There’s lots of people that are anti-fracking. They don’t even understand what it is or what the real risks are. There’s a lot of people that are anti-pipeline and a lot of these things have gotten conflated.

Josh Young (00:06:09):

And I think when you remove the two and you understand kind of what’s happening, it becomes a lot clearer. And what we’re seeing is the impact of an arguably more than a decade downturn in long cycle oil investment, because we’ve been in this oil bear market, and that’s kicking in at the same time as this bust in shale where there had been three or 500 billion a year in, I think in some years, that had been spent, and in many cases lost, or much of the money was lost because of high declines of production at low prices.

Josh Young (00:06:43):

And so where you see those two meet, you end up with declining production, or at least production that’s not rising as much as you’d think, because you have this mini boom and bust along with this longer cycle. And it’s really, I think, messed up a lot out of the investment incentives. And I think it’s made this bull market for oil that we’re starting to see, way more powerful, as well as very misunderstood by many different sources.

Trey Lockerbie (00:07:09):

Well, on that note, maybe we just take a quick detour and debunk some of these things around fracking, because I’m not highly educated in it myself. And I could probably tell you that most people think fracking either creates dirty water because of the oil in the water, or the methane that could potentially come out of the fracking is bad, even worse for the environment than the carbon, et cetera. But I know there’s ways to burn off the methane now, even to say, power Bitcoin, which I think you have some familiarity with. So, what are some of these myths around fracking that we could debunk quickly?

Josh Young (00:07:39):

Yeah. Let’s address the two that you mentioned. So the first one is that fracking pollutes groundwater. And it’s hard to tell exactly where that started, but there was a famous movie, I think it was a decade ago called Gasland and they showed, I think it was Matt Damon going and finding tap water that was from a well in Pennsylvania. And they turned on the water on this one particular faucet and they lit it on fire. And this was a horrific misrepresentation of what’s happening. This was not at all related to fracking. There was zero relation. What happens in some places where there’s coal that’s naturally occurring near the surface is there’s a phenomenon called coal bed methane where if you pull enough water out from an aquifer that is surrounded essentially by coal, you end up de-pressuring the coal and you release natural gas from the coal.

Josh Young (00:08:32):

So they knew that. This was a total misrepresentation, but it looked really sexy and it fed into people’s fears, especially in New York City for their water system where they understand that there are some places historically where that water has come from that’s been really bad. Where there’s been all kinds of horrific industrial pollution and waste. Upstate New York there were historically all kinds of coverups and so there was a lot of sensitivity to this. But it’s also not a new thing. Fracking has been going on for decades and it’s been going on near population centers and near aquifers for decades. You look at near Dallas and you look in West Texas. This has been going on for a very long time in different forms, but essentially the same thing. And you can study these things and observe kind of the communication between different rock layers.

Josh Young (00:09:16):

And I think it was just this very easy kind of cheap hit. And unfortunately, as a society, we’ve been progressing from people that read books and long form essays, to seeing short kind of YouTube or Instagram or whatever clips. And it’s really hard to unsee the water being lit on fire, even though again, it’s totally unrelated. And maybe with like what’s happened with COVID and some other stuff, there’s more sensitivity to this where you see these videos of people vomiting blood and dying in China that were unrelated at all to COVID, but they were hard to unsee. So I think it’s a similar sort of thing. So I think that’s on the water pollution.

Josh Young (00:09:56):

And it’s not that it’s not affecting water at all. Any industrial process has externalities. So if you drill for oil or gas anywhere, you’re using stuff, you’re using equipment and supplying the equipment and running the equipment can cause small leaks. So you may have some engine oil that leaks. But it’s very similar to operating a commercial truck. And trucking, even trucking organic produce causes some amount of water pollution and some amount of emissions, but they’re not what they’re being described or being attacked or characterized. So there is a little bit of pollution, but it bears almost no resemblance to their critiques or the concerns that people have. And just the degree of risk versus the degree of concern is totally misplaced. And it’s really oriented towards anti-energy independence…

…Trey Lockerbie (00:19:53):

And as the price of oil increases, won’t that create a gold rush of producers to enter the market and with all these new rigs and eventually get enough supply so the price comes back down? Why would that not be the obvious case?

Josh Young (00:20:07):

Yeah. So that will eventually happen, but there’s a little bit of a couple of things going on that are going to make that hard. So one, we are at the tail end of a very long bear market for oil. We’re just starting this bull market. Prices, like you mentioned, in the last year have rocketed higher. And they’ve finally gotten to a point where it’s economic to start investing in these long lead time projects. The problem with long lead time projects is that they’re long lead. So in many cases you have to spend 10 years bringing your discovery onto production and developing it more. And there’s been too little activity in discovering oil fields. So you kind of need to start from the beginning. So in many cases you may need to spend 15 years in between now and bringing oil on. And oil prices have almost, I think they’ve doubled in the last year. So where do they go in between now and that kind of five to 15 year from now window for those long dated projects?

Josh Young (00:21:02):

And then on the short dated shale and other sort of conventional but short cycled projects, we’re just at the tail end of this giant boom and bust in that area too. And so there were many companies that misrepresented their economics and said, oh, we can break even at $30 oil or $40 oil or whatever their economics were. And many of those companies just reported their Q4 and they were profitable at $80 oil, but barely profitable. So it turns out that those companies require much higher prices too for their activity to be economic. And they’re only going to rush and drill a lot more if their activities are highly economic. So that whole, the setup both for the short cycle and long cycle, both of those are requiring much higher than historic prices in order to bring on new rigs.

Josh Young (00:21:51):

And then in the oil services industry, it’s even worse where there’s been even less capital available for even longer. I think people forget about this. They just kind of assume, oh, hey, there’ll be plenty of rigs. And there were more rigs running 10 years ago. The problem is that was 10 years ago and many of those rigs have been cannibalized. They’ve been scrapped. And many of the people that worked on them are no longer in the business. In many cases, they’re retired. And so getting the talented workforce, along with capable, additional rigs and frac stacks and other sort of equipment, it’s a real problem. And we’re not even at the point where it’s economic for those oil services companies to start. They’re starting to try to hire, but wages haven’t gone up enough yet, and they’re not even starting to build new rigs.

Josh Young (00:22:36):

So if you think about that from a lead time perspective, that’s a multi-year cycle on its own just for the short term stuff. So I think we’re set up for this multi-year bull market where the first thing you need to see oil services’ stocks go up 5 or 10 X. That way they can have an investment boom. That way they can go build over the next few years the equipment that’s necessary to have a drilling boom, to have drilling go way more than it needs over a multi-year period. And then you can have a big crash, but that might be coinciding with when these long lead time projects come on. So it’s really set up nicely I think for a very long, very strong bull market that’s really going to incent a lot of investment. But like you were saying, why can’t they do it? Well, there’s just all these logistical and investment problems that are keeping it from happening.

Trey Lockerbie (00:23:24):

Wow. Barely profitable at $80 a barrel. I find that very surprising. And especially when you’re considering the decrease in rigs, it’s not so much that the rigs are just going out of business and being scrap. They’re getting more efficient I think, say over the last 10 years. Or that would be the idea, right? Some innovation, they’re more efficient, and you would be able to run more profitably. So that kind of brings up for me, what is the actual marginal cost to produce oil today?

Josh Young (00:23:50):

So there is a cost curve. So it’s not like any one well. And I think that was the thing with shale where you had these various large cap or midcap companies with their CEOs getting on TV saying, oh, we break even at 25 or 30. They were talking about their very best well when they were drilling 500 wells and their 500th well was not economic at $150 oil. So there was a lot of this kind of snake oil-ish, charlatan-y, hey, we’re this, but we’re really that. And the truth was somewhere in the middle. And so I think it depends. I think the incremental well is going to be a lot less profitable than the average well. And since it’s a commodity, you really need that incremental well to be highly economic. So if there’s 500 rigs operating right now in the US for oil and gas drilling, to bring on that 501st rig needs to go somewhere. Needs to have a producer for whom the return is likely to be in excess of their cost of capital. And for producers right now, there’s huge pressure on them to return capital and not drill.

Josh Young (00:24:53):

Again, we’re at the tail end of this disaster where every company lost a ton of money that was active in the space. And so there’s this very, very high bar for them to bring on that rig. They have to find the rig, and there are some rigs left, and then they have to find the people for that rig. They have to find the oil field tubulars and other equipment, which is sold out in many cases. And then they have to have the drilling inventory. They have to have the rights to land that’s economic enough to exceed the costs of all of those different things.

Josh Young (00:25:23):

So on the marginal well, there’s a pretty good argument that you’re just getting your kind of 10% return on a cost of capital adjusted basis when you factor all that in. So the rig count is rising, because you are getting to that point, but you’re not so far beyond the point that you have these companies going and ordering more rigs or getting longer contracts from anymore. And in general, I think there’s this trajectory of a slow build, but it’s definitely not boom time, even with oil, as we’re talking around $96 WTI.

Trey Lockerbie (00:25:57):

So you have these green energy ESG initiatives underway, COVID shutdowns, labor shortages, as you mentioned, a lot of people exiting the space and it’s leading to this gap between drilled uncompleted wells, and completed wells. And this might sound very technical to a lot of people listening, but I’m having fun nerding out on this stuff with you. And I feel like it’s really setting up this bullish argument for this commodity here. So I want to kind of quickly walk us through what that means, the difference between the uncompleted and the completed wells, and why that is and the incentives driving these decisions from producers to kind of curb the investing in additional production.

Josh Young (00:26:35):

That’s a great, really kind of important choke point for the industry. And I guess I’ll just say it’s similar to the rigs where when you had under investment, you didn’t have, especially in the last couple years, you didn’t have companies building more rigs. And since they weren’t building more rigs, there’s a certain number of hours that a rig can work before you need to replace the engine, you need to replace various other components, you need to replace filters. And at some point you just hit your useful life on a rig and you’re done. And so that’s kind of an analogy to the process from a producer’s perspective, going from undrilled land to a producing well. And one of the steps is after you drill the well, then bringing the right equipment on to frack the well and tie it into a pipe and bring it on production.

Josh Young (00:27:21):

And so as a part of this giant boom and bust and the short cycle shale stuff, there were a lot of wells that were drilled that weren’t completed and brought on yet. I think some of it was a capital budgeting and timing thing. Some of it was some of these wells were not very good and they knew they weren’t good. And so they didn’t even bother fracking them and bringing them on. And what you’re pointing out is a white paper we talked about, and there’s various other sources that have been focusing on this because it is an issue, where we noticed that the number of these wells that were prepared to be fracked but hadn’t been fracked yet, was falling a lot. And what that told us and what it tells us in terms of why things are going to struggle to scale how you would expect in a boom, is that there’s been essentially this underinvestment, essentially burning the furniture where the wells that were drilled already are being completed faster than new wells are being drilled.

Josh Young (00:28:13):

And that means that you need to drill a lot more wells in order to be able to complete the same number of wells that you’ve been completing. So if you think about it, step one, step two, oil. Well, they did too many step ones to start, and now they’re doing too many step twos and you need to kind of coincide step one and step two in order to get to a completed well that’s on production. So it’s a sequencing issue, but it’s also a budgeting issue and we’re seeing many producers now subsequent to that white paper, we’re seeing them come out with guidance where they’re raising their capital budgets anywhere from 20% to 25% without raising their production guidance at all. Some of that’s cost inflation, but some of that’s also replacing. They’re recognizing they did not enough step one drilling wells. And so now they have to do more step one in order to catch up with the step two, which is completing wells.

5. Morgan Housel – The Best Story Wins (EP.100) – Jim O’Shaughnessy and Morgan Housel

Jim O’Shaughnessy:

That’s fantastic and it’s another thing we share. I generally think of myself as unemployable, other than by myself. Sometimes even I don’t want to hire me because I’m such a pain in the ass for everyone involved. But that’s a really cool situation to have and at least my impression is that your colleagues understand the new world we’re in. By that, I mean like some lawyers get when Patrick wanted to do, Invest Like The Best. They were like, “Yeah, but this isn’t OSAM business.”

Morgan Housel:

Of course, it is. It’s a key integral OSAM business.

Jim O’Shaughnessy:

Exactly.

Morgan Housel:

People don’t understand. The quirk that people don’t understand about what I do in Collaborative Fund too, is I never write about things we do at Collaborative Fund. I never say here’s the deals that we did, here’s why we’re so much better than everything else. I could, I have those stories that I genuinely believe but nobody wants to read that. That’s the truth. People don’t want to read what is clearly marketing but they will want to read and share with their friends and forward onto their coworkers an article about something that has to do with investing or history or psychology. I just want to write things that people will want to share. If I do that and gain the largest audience, cast the widest net, people will learn through osmosis about what Collaborative Fund is. That is so much more effective than force feeding them by saying here’s why we’re so great, here’s why we’re so great. I feel like a lot of asset managers that have finally woken up to we need to have a blog, we need to have a podcast. They still do it wrong because what they write about is how good they are and why you should give them their money. Nobody wants to read that.

Jim O’Shaughnessy:

I could not agree with you more. Luckily, Patrick and I are so simpatico on this. It’s just like you know what? Nobody gives a fuck about you. Really if they do, they want to know how can you help them? How can you give them something that’s interesting that might not be in their toolkit? You’ve got to be useful and the way to be useful, in my opinion, is to be an honest broker. About hey, have you thought about this, this or this? So whenever, for example, when I’m commenting on anything about OSAM, I always lead in with talking my book. I want people to know with that line, I’m going to throw a little marketing at you here…

…Morgan Housel:

No, I think it’s obvious too and I’m happy to admit this. There’s nothing new or groundbreaking in the slightest in the book. The book’s message is like don’t be greedy, compound interest is awesome, save some money. This is not rocket science stuff. But if I think why it may have connected, it’s because I tried to tell a story around that. A thing that I really believe is true for all, everything in the world is that the best story wins. It’s not the best ideas, it’s not the right ideas, it’s not the complex ideas. It’s just the best story wins. I’ve used this example before of Ken Burns, the documentarian. His documentary on the civil war came out in 1990. When it came out in 1990, it was such a success. More people watched the civil war documentary in 1990 than much the Super Bowl that year. It was just like a ridiculous blowout success.

This is a documentary on the civil war, which is like one of the most documented. How many books are there on the civil war? Thousands and thousands. There is nothing new in Ken Burn’s documentary, nothing new. This is not like he was the guy to uncover Gettysburg. There’s nothing new in there, he just told a really good story about it. An amazing story with captivating music and amazing editing. Because of that, he took an event that everyone had known about, and everyone has known the detail about. He got more Americans to tune in than watch the Super Bowl that year.

I think there’s so many examples of that, of things that everyone knows, have been discovered for centuries. Nothing’s new but if you can tell a good story about it, you’ll get people’s attention. That is what I think a lot of academics, in particular, miss. Is that they have all the right answers but they are the worst storytellers. I think a lot of the times they go out of their way to be bad storytellers. They want to use big words to fit in with their colleagues, to fit in with the academic tribe. I think there’s so much room to take what academics know and explain it to a layperson in a story that they’re likely to remember and likely to hook onto. There’s so much room doing that.

I think you could also write a book, not just Psychology of Money but you could write the Psychology of Medicine, the Psychology of Politics, the Psychology of Sports, the Psychology of Relationships. Just talk about things that people intuitively know and tell a story around it in a way that would really connect with them. So that’s what that I’ve always tried to do in my writing. Is like I don’t have the intelligence, the brain power, the education to discover new things in finance. Even for the people who do, I think there’s probably not that much to discover left. We’ve overturned almost all the rocks but I think there’s still a lot of room to be made and progress to be made. Connecting with these people by just doing a better job telling the story.

Morgan Housel:

I would, because this is the magic wand. I’ll make this ridiculous. I would show people exactly in their life when the things that they admired about themselves were actually due to luck. And I would show everyone a movie of like, “Hey, this point in your life that you think you did this.” Actually, here’s what happened behind the scenes. You didn’t know about that actually led to that thing. I think that would instill a degree of humility in people that would be so beneficial. It would help, it would not depress them. It would be so beneficial to know. And also I would, so this is a magic wand. I would show them every one else in the world’s movie too. I’d be like, “Here’s all the areas where Jim got lucky and Morgan got lucky.” And then they would stop idolizing people for just some level of success. And they would look at individual actions that led to what actually they did on their own volition to actually get to where they were.

Because I think one of the biggest problems in the world, not one of the biggest problems that’s exaggerating, but a problem in the world is that we underestimate the role of luck in a massive way. And even there’s that saying of like, “The harder I work, the luckier I get.” I think that’s bullshit too. I think luck is just luck. And I think if you are working hard to become luckier, then that’s actually a skill, luck is just luck. For you and I, you and I are white American males born in the latter, half of the 20th century, that’s just luck you. You and I did nothing to do that, it’s just what happened. And I think everyone has some story like that they under appreciate. And to make them aware of it would be a huge help in the world.

6. Amazon CEO Andy Jassy Speaks with CNBC’s Andrew Ross Sorkin on “Squawk Box” Today – Andrew Ross Sorkin and Andy Jassy

SORKIN: You talked about chips being a major issue. What do you think we should be doing here in the United States about manufacturing those chips and does Amazon have a role in that long-term you think?

JASSY: Well, I think it’s, it’s, it should concern people that so much of the chip production is concentrated in one place, and there’s, you know, there are a lot of geopolitical things that could happen. And so I think it’s quite wise for the US to be thinking about creating more production here and, you know, I’m very happy about the CHIPS act that we’ve been working on in the country. It’s a lot of money, it’s $35, $40 billion and yet, it’s probably not enough. I think we probably are going to need even more than that to have the ability to withstand some kind of shock to production in a particular part of the world. But I think it’s very important. I, you know, we design our own chips and we’re big buyers of chips and we’re big customers of some of the big chip companies as well as producers ourselves so there could be a role for us to play. We certainly want to help and we certainly want to partner.

SORKIN: Do we believe that the companies in America and I know Intel is trying to do this, but do we have enough know how in this in the country to actually do the manufacturing piece of this do you think?

JASSY: I think it’s a good question. I think we have a start. I mean, Intel obviously has been doing this for a long time. And you know, Pat Gelsinger has been a partner, you know, first on the VMware side now with Intel for a long time and I have confidence in their ability to produce and but they have work to do  as they know and and we’re going to need additional providers I think to be where we ultimately want to be…

…SORKIN: In that context, how do you see the union movement that’s taking place, frankly, around the country, but clearly aimed in certain places and I’m thinking about New York, where I’m from at Amazon?

JASSY: Well, I mean, I’d say a few things. You know, first of all, of course, it’s its employees’ choice whether or not they want to join a union. We happen to think they’re better off not doing so for a couple of reasons at least. You know, first, at a place like Amazon that empowers employees, if they see something they can do better for customers or for themselves, they can go meet in a room, decide how change it and change it. That type of empowerment doesn’t happen when you have unions. It’s much more bureaucratic, it’s much slower. I also think people are better off having direct connections with their managers. You know, you think about work differently. You have relationships that are different. We get to hear from a lot of people as opposed to it all being filtered through one voice. If you want to keep the construct that we’ve had for for this long, you have to have, you know, competitive and compelling benefits though for for employees and it’s why we champion the $15 minimum wage a few years ago and we’re up over $18 now. It’s why we have full insurance, why 401k, 20 weeks of paid leave and our Career Choice Program where in our fulfillment center for our employees who want to get a college education, we’ll pay for their full tuition, so those things really matter. The one thing regardless of how it all evolves is we just won’t compromise on the customer experience. That for us, you know, is paramount…

…SORKIN: When you look at one of the issues that the unions have raised as you know so well are safety issues, and you’ve addressed this to some degree in this morning’s letter. But I’m hoping you can speak to it because there was some data out just about two days ago that seemed to suggest and this was data put together by I think some of the Union advocates that there were more, even double the number of injuries at Amazon facilities relative to their peers.

JASSY: Well look, there’s a lot of ways you can spin the safety data and some special interests folks like you’re talking about with this case, will do it for their own interests. That that data is not really accurate. You know what I would say is a few things. You know, first of all, for anybody that had hired a lot of people during the pandemic like we did, and there are plenty of others who did as well, their incident rates, their recordable incidents which what OSHA asked everyone to report on, went up in 2021 versus 2020 because he had a lot of new people. In our case, we hired about 300,000 people just in 2021, most of whom had never worked in this type of manual and industrial space, and who had to be trained and all the data we have says that the incidence of injury in the first six months is always much higher than thereafter. So we have a lot of new people, you’ll have more incidents. But that said, if you if you look at the the injury data and safety data, you know, for us, we we have a few macro areas in which we do work. We have what OSHA calls warehousing. We have what OSHA calls messengers and couriers, messengers and couriers, and then we have grocery and if you look at the industry average versus our numbers, we’re a little bit higher than average in warehousing, we’re a little bit lower than average in both messenger and couriers and grocery. So we’re about average, which, frankly, I take no solace in. We don’t aspire to be average.

You know, we’re trying to be the best in the industry and it’s why we’re spending, you know, we have we spent about $300 million on safety last year alone. We have about 8,000 people who just work with safety and we’re trying all sorts of things and work in all sorts of all sorts of things. We have a rotational program we built where we’ve built sophisticated algorithms to try to predict when somebody’s doing something too, too frequently and rotate their jobs and rotate what they’re working on. We have wearables that we’re investing in that send haptic signals when we believe you’re making a dangerous movement. We have, you know, new shoes that we’ve had everybody wear that, you know, protect your toes and avoid slips. We do training on body mechanics and wellness. So we’re working on a lot of those things, but the reality is that we will not be happy until we’re the best in the industry and and even then, I won’t be happy because I’m gonna know there are things that we could be doing better. This is important to me, it’s important to—

SORKIN: How do you think about this? So one of the things that Jeff said in his letter last year was that one of the missions of Amazon now is to be Earth’s best employer and Earth’s safest place to work. How do you think about that relative to the priority of serving the customer?

JASSY: I don’t think they have to be at odds. And in fact, I think they’re very complimentary. When you take care of employees and employees are safe and they love working where they work, they stay longer. They tend to be happier, they tend to be more productive. And all those things improve the customer experience. So I see them as very complimentary…

…ORKIN: On the platform. Before we let you go, it’s been 10 months now in this new role. And I’m curious what the relationship is like with Jeff, how much time you guys spend together, what does he think of all of this? We were actually mentioning we thought your letter was a little Bezosian in some respects. What’s it been like?

JASSY: Well, I have a great relationship with Jeff and, you know, I’ve known him for a long time and I have an unbelievable amount of respect for him. And we talk regularly, we talk weekly and it’s great to have a sounding board and he’s got so much wisdom. And you know, I think both of us share a lot of excitement and optimism for the future. We’re so early in all of our businesses. I mean, even in our retail business, which people think as kind of our most mature business. You know, we’re about 1% of the worldwide retail market segment and 85% of retail still lives offline. So we’re so early in all of these areas. You know, AWS is a $70 billion revenue run rate business growing, you know, about 37% year over year in 2021. And still 95% of the world’s IT spend is on premises and not in the cloud. So, all of these areas you go through it with Alexa has the chance to be kind of, you know, the best personal assistant which changes your life. And entertainment as we just talked about. Our advertising business is early. Kuiper, you know, we’re building a low Earth orbit satellite. And Robotaxi business in Zoox. I mean, we’re so early in these areas that I think we both share a lot of optimism that there’s an opportunity to change a lot of customer experiences over a long period of time.

7. An Interview with Adam Mosseri About Creators, Blockchains, and TikTok – Ben Thompson and Adam Mosseri

What was this exactly, though? I mean, on one hand you are obviously the head of Instagram, so you don’t say anything publicly on accident. On the other hand, I don’t think that there was any sort of product announcement here. What was this talk, in the broader context of your day job?

AM: I believe that a lot of these conversations are going to happen with or without us. You see me out there a lot, probably on Twitter and elsewhere, doing talks sometimes but often engaging in other ways, because I just think it’s important to engage in the conversation because it’s going to happen with or without us.

I think one of the more interesting conversations over the next five to ten years is how power is going to continue to shift. I think technology has shown over and over, over centuries, that it tends to take power from the establishment and give it to people. It’s not a direct line, there are always detours, but if we assume that’s going to continue to happen, if you look at the fierce competition out there, particularly for creators, you assume more challengers are going to be interested or willing to hand more power over to creators. I assume the incumbents will follow.

Then, I think we should be part of the conversation of what that world looks like. I think, as uncomfortable as it might be, we should embrace it. I think ultimately, over the long run, we should take a view that what is best for creators is best for platforms, because there’s going to be more creativity in the world. There’s going to be more exchange of ideas, there’s going to be more art, there’s going to be more content, and we should try and figure out what that world looks like. The main idea here is just to throw out two longer-term ideas and hopefully influence that conversation…

…You talked at the beginning of your talk, and you reference it here, about how the Internet broke down gatekeepers but then “unexpectedly”, your words not mine, we ended up with even larger platforms like Instagram. Obviously that’s been the core thesis of Stratechery, is that actually all this stuff goes in the opposite direction people think.

AM: Aggregation Theory.

Yeah, exactly, that’s exactly what it is. Is Instagram a gatekeeper? Is it just a super-gatekeeper?

AM: I think that the Internet has very clearly pushed power into two directions. It’s pushed power into the hands of more and more people, not just creators, but I mean it’s enabled all sorts of businesses like yours, and it’s also pushed power up into really broad platforms like Instagram. I think the big companies, or what we used to think of as the big companies, have suffered the most. There’s just been these über-sized companies. I do think, though, that large platforms, if you look at the next ten or twenty years, they’re going to rise and they’re going to fall. When they fall, they’ll fall slowly, but I think they will fall. They’ll slowly lose cultural relevance, and —

But why? What’s going to be the driving factor? This is the big question. You talk about this as if it’s a law of nature, that creators are going to take over, but what’s the causal function here?

AM: Probably I think it’s really going to be competition. Take TikTok, for example. TikTok is a behemoth, I actually don’t think most people realize how big and relevant TikTok is, if you look at how much time people spend or how total minutes on TikTok in a day compare to most of the competition.

I was told you have no competition, you’ve killed it all.

AM: (laughing) Oh, yeah. Well, it doesn’t feel that way on my side! I know there are a lot of people who disagree with me, it certainly doesn’t feel that way over here! YouTube is also a behemoth. Actually I think’s TikTok’s a really good example, I’ll give a lot of credit to them for some things they’ve done well.

I think the newer platforms are going to see how important creators are. I’ll talk to a couple of reasons why I think creators are important. We’re in a world where clearly we’re inundated with more and more information, and there’s value in aggregators to help us find the most valuable information, that is sort of an adjacent concept to Aggregation Theory. One effect of that is, yes, aggregators have value, but another is that people are less and less interested in processed content, they want to get more of a sense of authentic content. I’m not saying creators are all authentic, obviously people bring a certain part of their identity, not their whole identity online, but people are much more interested — and we see this in engagement data — in seeing what it’s like to be in someone else’s shoes, seeing what it’s like to be backstage before a political debate or warming up before a football match or in a green room before a TV spot. They want to see the world through other people’s eyes and they’re more interested in creator-focused content, someone’s point of view, whether it’s you sharing your analysis on a business or The Rock pontificating or a small country artist from Nashville showing a song that she’s working on.

You’ve seen that one of TikTok’s strengths has been how strong they have been at breaking new talent, how well they have done by the little guy, the small creator. They have leaned much more into exploration-based ranking than pretty much all the competition, or least earlier, and they’ve helped new talent break. Now, it’s not all perfect over there, I think that there’s a lot of volatility and there’s a lot of downsides too, but they’ve done really well by particularly smaller creators and I think you’re seeing the competition follow. You’re seeing the other major platforms that you can think of, or you would’ve thought of two years ago as incumbents, which now I think you might actually even think of as challengers, follow, and I think you’re going to continue to see that.

My take is, and I could be wrong, but my take is that over the next five years, ten years, you’ll see more platforms, both challengers and incumbents, be willing to hand more power over to creators. I think that’s the causal relationship, is competition, but I think there’s also some extrapolation of existing trends…

…I think you just got into what I see as one of the issues here, because the Web2 people tend to have Web2 solutions, the Web3 people have Web3 solutions, when it’s always been at least clear to me that this token idea has always been the most attractive and interesting thing about blockchains. You can pay for a token with a credit card, there’s no reason why it has to be an all-in-one system, and this idea that it has to be full stack up and down all Web3 under the blockchain doesn’t make sense. Believe me, I know a lot about database performance — that’s why there’s been very little news about Passport over the last year, fixed now — but you’re not going to be doing a lot of this stuff, certainly not on a blockchain.

The idea that you could have all Web2 infrastructure, but this one piece that to your point, you can carry around from place to place, I mean, I’m not being a very good questioner here because I’m sort of making the point, I think this is what is very attractive, having this piece that no one controls. But to your point, someone needs to build it. You didn’t do a product announcement, you just painted a vision, is this though sort of a backdoor announcement of Meta’s new blockchain play? Are you going to help sort of build this infrastructure?

AM: I think we’re definitely interested in it. To be totally transparent, part of the reason why I want to talk about it publicly is to apply some pressure and get some excitement around the idea and build some momentum. I can’t talk about or I’m not going to talk about the specific companies I’ve been talking to, but I’ve been trying to talk to as many people as I can at all the different levels; at the payments level, at the authorization level, at the platform level. There’s a lot of interest, but to make this happen is far from a sure thing. It’s like you’re trying to align a bunch of different cultures and a bunch of different sort of philosophies around this idea.

The biggest risk to the idea, I think, is, is there enough of a market fit for creator subscriptions that this idea would create enough incremental value that those involved, particularly the platforms at the Instagram layer, not the sort of payments layer, will believe that it’s going to create enough incremental value that they won’t need to over-worry about their particular share.

It’s not lost on me that a lot of people don’t trust the company that I work for or me even. And so in all of these conversations, they’re trying to figure out what my angle is and I’m like, “No, no. I just think this should exist. I think it’ll be good for us indirectly over the long run.” I think if we get critical mass, if we get enough platforms to do this, then there’s pressure for the holdouts to do it because the creator community will put pressure on platforms to support this. But the question is, “Can you get to critical mass?” And I think the biggest risk with getting to critical mass isn’t the technical one. Like you said, we don’t have to build a whole thing on-chain, sure, you could pay with this with coin if you wanted to, but you could totally pay with it with fiat.

The stack should be 98% Web2 technologies and like 2%, or actually probably more like 0.2% on-chain. People, when they talk about blockchain, you only want to use it for what it is uniquely suited to do, and what it is definitely uniquely suited to do is to be a neutral arbiter between platforms where it is the one place you can go that no one controls, no one touches, and you can stick a token there and that token has the minimum amount of information necessary. Believe me, I’ve thought a lot about this, but no product announcements for Passport here either!

AM: The question is what’s the token? What’s that protocol? What exactly does that token entail and how do we make sure that it supports enough use cases that enough platforms and businesses will be interested in supporting it, right?

Let’s drill into this point because I think this is the biggest question. So from my perspective, the value that Instagram brings to the creator ecosystem is, Meta in general is by far the best customer acquisition platform, period. There’s no one even close. And I would say that’s the case, even post ATT. It’s instead of a thousand times better, maybe it’s a hundred times better, but it’s still really good. TikTok, you have discovery of new talent, Instagram, you can acquire customers, and YouTube is where you actually make money.

I think you talked about creators start on Instagram, and they go to platforms like YouTube, and to me this is because YouTube monetizes so well. One of the brilliant things that YouTube did and Google did, and it took many, many years to build up, is they shared a huge chunk of the revenue with creators, and every single creator in the Internet knows that outside of subscriptions, the way to make money is to get on YouTube.

And so the question is, given YouTube is so dominant here, to me, they’re the great white whale, I would love to have a Passport integration with YouTube, they’re so far ahead in this particular area, why would they ever want to partner with anyone, number one? Number two, that suggests that Instagram needs to get way better at monetizing its creators so it’s a competitive counterweight, but then we’re back in, “Well, you’re in your walled garden, they’re in their walled garden.” There’s a valley of disconnect here, and how do you think about crossing that chasm?

AM: So a few different things. On the Instagram side to start, I think there’s two ways it can benefit our business. Certainly we’re a customer acquisition channel and we’re good at that, but also, it’s the same idea, but not paid ads, we are a marketing channel for a lot of creators. Creators share a bunch of content and tell a story, build an audience, and then they monetize that audience, whether it’s through rev share on YouTube or branded content deals on Instagram or subscription on Twitch, and they drive a lot of impressions for us. So you don’t even need to pay us directly for you to create value for us and for us to create value for you. If we are a great platform for you to build an audience, then you’re going to be creating compelling content and we can advertise against that content the same way we advertise against everything else. So it doesn’t even have to be ads.

I agree. Just to say it’s just an ad platform — an ad platform only exists in the context of great organic reach.

AM: Well, I want to point out both because I think this is true for any of the platforms like us. I think YouTube is I think one of the big questions because they are — if TikTok is the best at breaking new talent, YouTube is the best at driving direct dollars into creators’ hands. I think if you look at the branded content ecosystem on Instagram, it’s probably about the same size. It’s many, many billions of dollars in your industry.

The same size as YouTube money or YouTube-branded content?

AM: I don’t know what YouTube pays out creators, I’m just talking about rev share. I don’t know what the total is because I don’t think they’ve released it, but I’m just saying, there’s other big things, but I don’t think anyone who’s a creator who sells branded content on Instagram thinks of that as Instagram service. They think of that as like, “No, that’s my deal. I made that happen on the side,” even if we help.

Even though it’s definitely an Instagram thing.

AM: Yeah. We’re just not going to get credit for that. 

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Disclaimer: The Good Investors is the personal investing blog of two simple guys who are passionate about educating Singaporeans about stock market investing. By using this Site, you specifically agree that none of the information provided constitutes financial, investment, or other professional advice. It is only intended to provide education. Speak with a professional before making important decisions about your money, your professional life, or even your personal life. Of all the companies mentioned, we currently have a vested interest in Amazon, Apple, and Meta Platforms (parent of Facebook and Instagram). Holdings are subject to change at any time.

We’ve constantly been sharing a list of our recent reads in our weekly emails for The Good Investors.

Do subscribe for our weekly updates through the orange box in the blog (it’s on the side if you’re using a computer, and all the way at the bottom if you’re using mobile) – it’s free!

But since our readership-audience for The Good Investors is wider than our subscriber base, we think sharing the reading list regularly on the blog itself can benefit even more people. The articles we share touch on a wide range of topics, including investing, business, and the world in general.

Here are the articles for the week ending 17 April 2022:

1. RWH004: Intelligent Investing w/ Jason Zweig – William Green and Jason Zweig

Jason Zweig (00:06:24):

I think the other story about my dad that really sticks in my memory, William, is in 1981, when my dad was dying of cancer, I was home for a visit and the phone rang, and a voice said, “Is this the Zweig residence?” Very polite, formal sounding man. And I said, “Yes, can I help you?” And he said, “Is Irving there?” And I said, “Yes, but he’s not really able to come to the phone, can I take a message?” And as I recall the man’s name he said, “Well, could you tell him that Glen Irwin is on the phone?” And I knew everything about my parents’ business and a lot about their life history, I had never heard of this man. And I went and I told him. At that point it was very difficult for my dad to move around the house because his lung cancer had spread to his legs, but he looked at me and then a light came on in his eyes and he said, “Oh, I’ll speak to him.” And he, with a great deal of difficulty, came to the phone.

Jason Zweig (00:07:28):

If you’ve ever listened to a stunning conversation that you can only hear one half of, it always sticks with you. And my dad took the phone and he said, “Glen,” and after a long pause my dad said, “Yes, I remember.” And the person at the other end started telling my dad a story, and my dad kept nodding and saying, “Yes, I remember, I remember” and I saw something I had never seen, I saw my father cry, and I couldn’t hear almost anything of what Mr. Irwin was telling him, but they talked for about 10 minutes And at the end my dad said, “Thank you very much, I hope so,” which I immediately inferred, and I think correctly, that Mr. Irwin had said to my dad, “I hope I will get to see you while I still can.”

Jason Zweig (00:08:22):

And when he hung up I said to my dad, “Who was that?” And my dad proceeded to tell me the other half of the story, which is sometime around in the late 1930s, my dad was the student at Union College in Schenectady in New York, and he was walking to class one morning, and he was walking behind a student, and my dad noticed he was black. And at that time he was either the only black student, or one of maybe three black students, or a handful of black students at the time, and my dad had never seen him before. And they were both walking along, minding their own business, and suddenly from behind a few trees a bunch of white guys jumped the black student and started kicking him and beating him up. And my dad immediately dropped his books, or whatever he was carrying, and jumped in and fought back and took Glen Irwin’s side, even though he didn’t know who this kid was, but it was obvious to him who was right and who was wrong.

Jason Zweig (00:09:26):

And momentarily the campus security people came along and broke up the fight, and they all got dragged to the office of the president of the university, whose name was Dixon Ryan Fox, who was a very famous scholar. And of course the white kids who had jumped Glen Irwin all blamed him, and they said, “We were walking along, minding own business, and this N-word guy attacked us, so we had to fight back, and then this kid came along and made even more trouble, and that’s what happened.” And so Fox turned to my dad, and Glen Irwin, and said, “What’s your side of the story?” And Glen Irwin was so scared he couldn’t speak, and my dad said, “Well, President Fox, maybe you remember me from when I was admitted to Union College,” because my dad had gotten a rejection letter when he had initially applied that said, “You’re qualified for admission but the Jewish quota is filled,” because in the 1930s most elite educational institutions in this country had a policy that they would only admit so many Jews, and the Jewish quota had been filled.

Jason Zweig (00:10:37):

And so my dad immediately got in his family’s wagon, because in those days they didn’t have cars, and rode to Schenectady, which was probably about 25 miles away, 30 miles away, and he waited outside President Fox’s office all day long until his secretary said that he could go in. And he was admitted, and he said to the president of the college, “You sent me this letter, and it said the Jewish quota has been filled. Well, as you know, President Fox, the whims of war are gathering in Europe, and young American men may be called into military service. Should I tell the US Army that the Jewish quota has been filled when I’m drafted?” So he’s telling this story, and President Fox says, “I remember you young man, why don’t you tell me what really happened?” And so what happened in the end was the thugs who attacked Glen Irwin were expelled. Glen Irwin went on and, if I remember right, he became something like a chemical engineer, and became a senior executive at a major company in the US. And what to me was so striking about this story is that my dad had never told any of us about this. My mom had never heard the story, in fact, the day it happened my mom didn’t even hear about it, because all this happened between me and my dad, and that, I think, is really the definition of quiet courage, when you do something that noble and you never even talk about it. And he completely transformed this man’s life, and obviously Mr. Irwin was calling because somebody had told him, “Irving Zweig is very sick,” and they hadn’t spoken in over 40 years…

…William Green (00:31:20):

And I wondered if you could talk about the element of luck versus skill. Clearly these guys have to have skill. I remember people telling me that they had been in investment meetings with Peter Lynch at Fidelity, and they would say, “Look, I came out of the same meeting. I heard the same information from the same companies and he made more money than I did again and again.” So there was clearly something he had. And yet there is an amount of luck that I think we can’t deny. Can you unpack that a little for us?

Jason Zweig (00:31:47):

One way I like to think about it, is that there’s a skill to being lucky. And I know you’ve heard me tell this story before, William, and technically it has nothing to do with investment management. But people often ask me how I got to edit Graham’s book, The Intelligent Investor. And they expect me to say, “Oh, the publisher did a beauty contest and brought in 10 different writers and had each one write a sample chapter.” Or, “They interviewed people,” or whatever. And it’s like, “No, that’s not what happened at all.

Jason Zweig (00:32:19):

What happened is this. So I had read a book and then interviewed the author, a book called The Luck Factor by British psychologist named Richard Wiseman. And he had done a sort of big nationwide survey of people’s attitudes toward luck. And when all the surveys came back, he and his team were going through them. And there was one that really jumped out at him, which was, and I’m massively really paraphrasing. I’m going to get all the details wrong. But the essence of it is correct.

Jason Zweig (00:32:49):

This woman had said, “My husband died. Two of my kids have cancer. I lost my job. I got it back, but I’m a very lucky person.” And he said, “I really need to interview this woman.” So they brought her in and he said, “You described all these terrible things that happened to you and you say you’re lucky. Why do you say that?” And she proceeds to tell him this story. And she says that after her husband died and her kids got sick, she felt very depressed, as anybody would. And she was really struggling. And then she decided that she needed a rule. And the rule she came up with was whenever she’s about to go into a room full of people, she thinks of a color.

Jason Zweig (00:33:37):

Then she goes into the room and she walks up to the first person who’s wearing anything of that color and says, “Hello, my name is,” whatever her name was. And so she looks at professor Wiseman and he looks at her and he says, “Well, what does that have to do with luck?” And she says, “I always have a date on Saturday night.” So I have just read this and heard the story from him. And there was a huge party at Time Inc. where you and I think both were working there at the time. And hundreds of journalists were there. I forget what the occasion was.

Jason Zweig (00:34:10):

And I was talking with as usual, my closest friends and not really socializing with the group. But before I had walked in the room, I had said to myself, and I’m not sure which color it was, but I’m going to say blue. I had said blue. And so I looked across the room and there was somebody I knew wearing blue. And I said to my friends, “Excuse me, I really have to go talk to her.” And it was our mutual friend, Nina.

William Green (00:34:39):

This is Nina Munk who’s a wonderful writer.

Jason Zweig (00:34:43):

Yep. And so I lost her in the crowd. And I haven’t talked to her in like three years or four years or something. And I was like, “Ah, the heck with it. Forget it.” And then I was like, “No, I have to talk to her because she’s wearing,” whatever color it was, blue. And I found her because I was for the color and we had a wonderful talk about nothing in particular and life went on.

Jason Zweig (00:35:06):

And I went back to work the next day, et cetera, et cetera. But it turns out a couple days later, her book publisher takes her out to lunch to congratulate her for finishing her wonderful book on the merger, the takeover of Time Warner by AOL.

William Green (00:35:21):

Fools Rush In.

Jason Zweig (00:35:22):

Fools Rush In. And her publisher says to her, “Oh-

William Green (00:35:25):

And we were working for those fools.

Jason Zweig (00:35:26):

That’s correct. And her publisher says to her, “Oh, Nina, you could help me with one thing.We have this book by this guy who’s dead, Benjamin Graham, I think his name is. And it still sells, but it’s old and we need to update it. Who do you think would be good for that?” And she said my name. Now, she insists to this day that she would’ve said my name anyway, but I’m not so sure about that. I think she might have said, “Well, I don’t know. There’s like five different people you could try. One of them is Jason’s Zweig.”

Jason Zweig (00:35:58):

But instead, because I just so happened to run over to her because she was wearing the right color, she said my name. And that’s why they hired me. And so the thing is, that was despite the fact that I was trying to outwork everybody else in financial journalism, despite the fact that I had all these great contacts, despite everything I threw into my job, why did I get this in honor of a lifetime? Because Nina Munk happened to be wearing a dress whose color I had thought of because I had read a book.

Jason Zweig (00:36:34):

So skill is hugely important and it matters, but much of life, maybe most of life is shaped by just these weird moments of random chance. And the more professional you are, and the more intellectual effort is involved in what you do, the more vehemently you will deny the importance of the luck, but it affects everyone in every field. And it’s hugely important in asset management too. 

2. Alexandr Wang – A Primer on AI – Patrick O’Shaughnessy and Alexandr Wang

[00:14:29] Patrick: I couldn’t agree more on that point, maddening that we don’t become just the perfect beacon for all the most talented people. The interesting analogy that I’ve heard before just to wrap our minds around the sorts of things or tasks or functions or whatever that constantly improving AI/ML models can accomplish. One model that was funny and interesting was like anything that an intern could do for you, you might be able to scale up through one of these models. It’s complicated enough that a person’s on it now but it’s simple enough that you give it to an intern it’s sort of repetitive. I always kind of like that conception. What’s your way of thinking about how to communicate to your audience, other businesses using your tooling and just people general, what categories of things AI can do well, and maybe what categories of things we’re excited about but might be a very long time until AI can do well.

[00:15:19] Alexandr: I think this is one of the general misconceptions about AI and machine learning, which I think causes a great deal of FUD. Which is that the intuitive belief is that the things that are easy for humans to do are going to be the things that are easy for AI and machine learning to do, which is absolutely not the case and the things that are easy for algorithms are relatively orthogonal, frankly, to things that are easy for humans to do. One simple example here is I think that it’s going to be a very, very long time before we have home robots that can do things like fold your laundry and your dishes, but a much shorter time span/I think this is already today where you can have artificial intelligence systems that are world class copywriters and can write better rhetoric, better words than most people ever could. There’s probably a few frameworks I would assign to this. I think in a broad general sense, one way to think about the potential impact or lower bound potential impact of artificial intelligence is kind of as you mentioned, which is the ability to scale repetitive human tasks. So take repetitive human tasks instead of going from zero to one, go from one to N human work. And I think that this is a generally amazing thing to happen because I think that humans for the most part don’t enjoy repetitive tasks or generally find those relatively unpleasant and find it much more exciting to be creative and to constantly be creating.

This ability to scale human tasks from one to N, is going to be this incredible, not only economic good or economic enabler for the world, but also going to be a significant enabler for humans to be more leveraged, more happy, more creative, et cetera. I think that’s one way to sort of contextualize the broad impact that AI can have. And there’s a bunch of other nuances, which I’m sure we’ll get to. If you think about what tasks humans are good at versus what tasks algorithms are good at, generally that more or less boils down to data availability, which is that where there are large pools of digital data an algorithm can learn from, and those pools of digital data either have been collected in the past or easy to collect in the future. Those are going to be the problems which algorithms can do effectively and can learn to do effectively. And then areas where there does not exist digital data and is expensive to collect this digital data. Those are going to be the last things to be automated. So a great example is if you look at GPT 3 and these large language models, the real secret behind it is that it leverages two decades of Reddit data, which is two decades of humans using the internet and basically typing language into the internet in various forms for decades and decades and decades. And that is a pool of digital data that it used to be able to do these incredible things in writing long form text.

Then if you think about this parallel that I mentioned around home robot, there’s so little data about actual capture data of let’s someone folding a shirt, or somebody folding a towel, or going around and doing chores, the ability to actually collect and produce that level of digital data necessary to produce algorithms that can understand that and actually perform this task is an incredibly, incredibly, incredibly hard road. This extends by the way to things that are really unintuitive. So for example, DeepMind and OpenAI very recently released algorithms some of which are very good at … DeepMind released an algorithm that’s very good at competitive programming, OpenAI released an algorithm that can prove very difficult math problems or math theorems. And these are both things which are very, very challenging for humans to do. Very, very premium skill sets as far as humans go. But there are incredible pools of visual data as well as abilities to verify or simulate the outcomes here, which allow these algorithms to reform actually incredibly, incredibly well. There’s this very interesting process by which artificial intelligence will slowly automate or meaningfully change what human jobs that are primarily digital in context will look like. And then a lot of the physical work will I think be generally on touch for a very long time…

…[00:20:18] Patrick: Maybe it makes sense to help people understand the process of creating one of these models in the first place. I think the discreet steps, let’s say the outcome is a model that makes a useful prediction. Ultimately, this is all predictions instead of what’s being generated by the models in the first place. I don’t know where to start, whether it’s with raw data or annotation of data, and we’re starting to get into what Scale now provides for companies. But how do you think about explaining the discreet or the important stages of building one of these models in the first place? I think just understanding that architecture will let us dig into each piece a little bit more.

[00:20:51] Alexandr: Again, everything starts with the data. I often will analogize the data for these algorithms as the ingredients that you would make a dish with, or the ingredients that you would make something that you’d eat with. Is incredibly, incredibly important. We often say this thing, which is data is the new code. If you compare traditional software versus AI software, in traditional software, the lifeblood is really the code. That’s the thing that informs the system what to do. In artificial intelligence and machine learning, the lifeblood is really the data. That is certainly like one major change. That’s really important. The life cycle for most of these algorithms is a few fold. So first is this process of collecting large amounts of data. By collecting it could be data that is already sitting there. There’s a lot of software processes that already collect a bunch of data. There’s a lot of cameras in the world that already collect a bunch of data, but you need to get the raw data in the first place. Then it goes through this process of annotation, which is the conversion of this large pools of unstructured data to structured data that algorithms can actually learn from. This could be for example, in imagery or video from a self-driving car marking where the cars and pedestrians and signs and road markings, and bicyclists and whatnot are so that an algorithm can actually learn from those things. It could be for example, in large snippets of text, actually summarizing that text so that now we can understand and learn what it means to actually summarize text. So whatever that translation is from unstructured data to a structured format that these algorithms can learn from, then it goes through a training process.

So these algorithms basically look through these rims and rims of data, learn patterns and slowly train themselves so to speak, to be able to do whatever task is necessary on top of the data. And then you launch one of these algorithms in production and you run them on real world data, and they’re constantly producing as you mentioned these predictions. The very important piece is, this is not a sort of like one way process this is actually a loop. If you look at almost every algorithm that has launched out their own production, it is not a sort of you build the algorithm and then you’re done because these algorithms are generally very brittle and unless you’re constantly updating them and maintaining them, they will eventually do things that you don’t want them to do, or they’ll eventually perform poorly. There’s this critical process by which you are constantly then replenishing them. You’re constantly going and recollecting new data, annotating it, training the algorithm, launching that new algorithm onto production and you constantly undergo this process to create very high quality algorithms.

[00:23:19] Patrick: I want to make sure that this interesting point you made about data being the new code really hits home for people, and maybe even put that in a business context. So if the IP or the moat of a software company is this code base that takes a very long time to develop, has all sorts of dimensions to it. Maybe it’s microservices, maybe it’s some code monolith, it’s questionably like an incredibly valuable asset. It’s digital, but it’s an incredibly valuable asset to the company. And you’re talking about, I think, a transition where it’s something different where maybe, I don’t know, maybe Google’s data repository or something, is this unbelievable advantage that they have because no one else has access to all of their data. Is that kind of what you mean? That ultimately maybe something like Google, their data is worth a lot more than their code base. And that that would become a trend that we see sort of across industries?

[00:24:07] Alexandr: If you look at the highest performing algorithms across a variety of different domains, image recognition and speech recognition and summarizing texts and answering questions of texts. So these very different cognitive tasks, look under the hood, they actually all use the exact same code base. That’s been this very meaningful shift that’s happened over the past few years in artificial intelligence. We’re at this point where the code has become effectively the same and more or less a commodity so to speak when it comes to artificial intelligence and machine learning. The thing that enables the differentiation is really the data and the data sets that are used to power these algorithms. To your point, if you think about … One of the ways that we talk about this in a business context is if you think about what is your strategic asset? In general in business, your strategic assets are the things that allow you to differentiate yourself against your competition. In a world where 99.99% of the software in the world is sort of traditional software, and then only 0.01% is AI software, then you care the most about your code. Your code is what will differentiate your product versus your competitor’s product or your processes versus your competitors’ processes, et cetera. But then as more and more of the software in the world’s written, infused with AI, using AI or over time the interfaces shift to AI. Interfaces and Alexa like interface for example, as that shift happens, as you go from 99.99 to 90:10 or 80:20, or even 50:50 over time, the vector of differentiation totally shifts to data and the data sets that you have access to. And so that means is that your strategic differentiator to your point as a firm is going to be primarily based off of what are my existing data assets.

And then what is the engine by which I’m constantly producing new insightful differential data to power these core algorithms that are actually powering my business. And these algorithms at the core that will power the future of business, I think are relatively core. I think there’s definitely algorithms around automating business processes that are going to result in significantly more profitable firms over time. There’s going to be algorithms that are based around customer recommendations and customer life cycle, which is a lot of the algorithms that we’ve seen date. Imagine TikTok recommendation algorithm, but for like every economic interaction or every economic transaction in your life that is constantly identifying the perfect next thing that you may want to transact with. And that is going to exist across every firm or every industry is basically going have to build their version of that. And that’s going to result in significantly more efficient trade. The long-term impacts of that you could think of as like a general reduction in marketing expenses or sales and marketing expenses because the algorithm just does a better job at knowing what the user wants to do next and having to do all this marketing and all this very active sales. There’s a lot of very real changes to I think the physics of what the best businesses will look like in, let’s say a decade or two decades or three decades that come from artificial intelligence. If you think about what will allow me to do these things better than someone else, it’s the quality, efficacy and volume of the data that is used to power these algorithms…

…[00:41:11] Patrick: If we zoom out and go to the more market side of things and put my investor hat on and think about what drives enterprise value, value creation, the things that investors ultimately care about when they’re putting money into a business, they want to get a lot more money out. The world of software has obviously been a center stage for seven, 10 years now because they’ve tended to be very scalable, fairly high margin, incredibly fast growing businesses. And the word that you never want to hear as an investor is deceleration, in the growth world where maybe they’re reaching saturation points and software is no longer a new thing. It’s a fairly mature thing. How do you think about, you mentioned this concept of thinking about like an S curve and maybe we’re for software approaching the diminishing part of that S curve. Where is AI in that same thing and how might these two things intersect to form lots of new enterprise value in the future if software becomes overly saturated?

[00:42:04] Alexandr: One thing to think about software for a moment, the sort of alchemy or the magic of software is that A, you’re able to collect very large scale data sets in a very coordinated way, B that you’re able to build simple workflow tooling on top of these data sets, think about your traditional CRM or frankly the majority of SaaS tooling is workflows on top of these data sets that enable business value. Then three is basically infinite scalability of a lot of these systems. These are some of the like technological primitives that have enabled SaaS broadly speaking, or software in general to produce a lot of value for most enterprises, but these primitives or these forms of alchemy have some cap, that’s for the saturation of software that you’re mentioning. Well, then if you think about AI technology and you use this mental model that I mentioned before, which is the fundamental promise of AI technology is you can take repetitive task that people are doing, you go from one to N with those repetitive tasks so you can automate the Nth repetitive tasks rather than relying on humans for that. Well, if you look at the majority of fortune 500 businesses or the majority largest enterprise in the world, there are an incredible number of parts of their business, where they spend enormous amounts of money on large teams of people to do repetitive tasks.

The alchemy that is possible there is not only the automation of meaningful parts of that work, but also the ability to even go further than even the best trained humans could do in many of those tasks. There’s some value that potential economic value or the TAM so to speak of AI machine learning is just absolutely astronomical. I think that is at minimum 10X, probably 100X the total business value that has been generated by SaaS systems or software historically, I think if you think about it, you have this one S curve of the saturation of software. And then there’s this very, very early S curve that is being developing right now around the productization and productionization of large scale AI systems, let’s say in the enterprise, or let’s say across businesses. And the real question is, okay, what’s the pacing of that S-curve versus the pacing of the saturation and deceleration of the current software S-curve? And I’m an optimist in not too long, we’re going to have a massive proliferation of AI use cases within the enterprise that are going to be way more impactful than the use cases of software in the past. And the way you’ll see that the business ROIs generated from high quality AI systems are going to be 10X more than the business value generated by let’s say, deploying a CRM or deploying an ERP system.

3. Why Market Timing Is Near Impossible – Peridot Capital Management

Let’s assume for a moment that you, unlike most everyone else on the planet, have an uncanny ability to forecast when S&P 500 company profits are going to decline within the economic cycle. You surmise that the market should go down when profits are falling  so you will use this knowledge to simply lose less money during market downturns than the average investor.

The long-term data would support this strategy. Since 1960, the S&P 500 index has posted a calendar year decline 12 times (about 19% of the time). Similarly, S&P 500 company profits have posted calendar year declines 13 times during that period (21% of the time). This matches up with the often repeated statistic that the market goes up four years out of every five (and thus you should always be invested). But what if you can predict that 5th year? Surely that would work.

Here’s the kicker; while the S&P 500 index fell in value during 12 of those years and corporate profits fell during 13 of those years, there were only 4 times when they both fell during the same year. So, on average, even if you knew for a fact which years would see earnings declines, the stock market still rose 70% of the time.

So the stock market goes up 80% of the time in general and in years when corporate profits are falling the it goes up 70% of the time. And so I ask you (and every client who I discuss this with), how on earth can anyone expect to know when to be out of the market? 

4. There Is No Playbook – Christoph Gisiger and Gavin Baker

Mr. Baker, you are one of the very few tech investors who lived through the dotcom crash in the year 2000 firsthand and remained active in the sector afterwards. As a battle-hardened industry veteran, how do you assess today’s market environment compared to back then?

Here’s the big thing: A lot of non-profitable tech companies with under $100 billion market capitalization just experienced a similar crash in valuations as we saw in the year 2000. But from a fundamental perspective, I don’t think the burst of the dotcom bubble has many parallels to what’s happening today. At that time, after the bubble burst, the fundamentals of every tech company imploded, they missed their earnings numbers by thirty, forty or fifty percent. Many had significant year-over-year revenue declines, and then their stocks went down more.

And how do things look today?

I do not believe that the fundamentals are going to crash in a similar way. In the year 2000, nobody knew which business models were going to work on the internet. The buildout in telecom equipment, data centers and software was not based on a consumption basis. It was built in anticipation of demand that took much longer than expected to materialize. In fact, we added so much telecom capacity that it took 15 years to absorb the amount of fiber and optical components we put in the ground. Every bank, every retailer and almost every other company was in a huge hurry to go online. They spent all this money to put up a website, but then they were like: «Wow! Why did I do that?» Today, you don’t see that degree of overbuild or excess on the supply side, because it’s all sold on a consumption basis. I promise, if the big cloud hyperscalers stopped spending on CapEx, they would run out of capacity in twelve to eighteen months. It’s a very different environment.

What does this mean for tech stocks?

It creates opportunities because today’s unprofitable tech companies are so much better than the ones back then. Many of them are Software as a Service companies where we know that the business model works. They have immense control over their P&L. You have seen some of them take their free cash flow margins up 80-90% in two quarters. They can be profitable whenever they want. They’re making a conscious trade-off between growth and profitability, and when they tilt towards more profitability, they don’t stop growing, they just grow slower. So it’s wild to me that you’ve had a move comparable to the year 2000 crash in non-profitable tech companies. Their forward multiples have compressed at least as much if not more, but they are great businesses, they’re not missing their numbers.

However, many of these companies were notably highly valued. As interest rates have risen, their shares have now come under pressure.

If you’re unprofitable, you’re essentially a long-duration asset. Hence, it’s natural that you take some pain as interest rates go up. But I think you’ve taken all the pain in the terminal valuation now. I don’t see much more multiple compression. Thoma Bravo, a private equity firm, just took out a software company at 12x forward sales. Today, you can buy a lot of software companies at roughly half that multiple, and they are growing faster with better fundamentals than the asset acquired by Thoma Bravo. So if you’re a software company trading at 6x sales, and an inferior company just got bought out at 12x sales by a very knowledgeable private equity buyer, I think that’s enough of a discount. That’s why I don’t see much more multiple compression. What will drive performance is growth and the relative operational performance of these businesses, and they should do reasonably well in an inflationary environment…

…How does this affect the outlook for the tech sector?

For their research, a lot of people are going back to look at the 70s. That’s a great exercise for energy, materials or restaurant companies where the business models are stable. But it may lead you to terrible conclusions for companies and industries where the business models have drastically changed. That’s why looking at the 70s to understand how tech will do today is absurd. Today’s tech companies are totally different, their business model is completely different. They have much higher ROICs, they are less capital intensive, have much higher margins, more pricing power and more gross profit per employee than tech companies in the 1970s. There is no precedent, there is no playbook for these business models in a high inflation environment. In America and Europe, you have never seen how inflation impacts the business models of different software companies. You haven’t seen how it impacts different internet business models. So from first principles thinking, there is an exciting opportunity to reach very differentiated conclusions and first principles thinking suggests these business models should do well fundamentally in a high inflation environment…

…In today’s world, cyberattacks are also occurring more and more often. Does this speak in favor of cybersecurity companies?

During the first twenty years of my career, I was always very negative on cybersecurity because it was one of the very rare industries where scale was a massive disadvantage rather than an advantage. Before the rise of artificial intelligence, human beings were writing software, it was a very manual process. And, as a cybersecurity company got bigger, hackers would start to optimize more and more for hacking that particular cybersecurity company’s software. As a result, the performance of that company would go down and it would lose customers. AI changed all of that because if the AI learns from the attacks, then you get better with scale. So that’s another area I’m excited for. Antonio Gracias, a fantastic thinker, has this great phrase «pro-entropic». To me, cybersecurity companies are pro-entropic. They benefit from rising chaos in the world.

5. Deep Roots – Morgan Housel

Forecasting, “If this happens, then that will happen,” rarely works, because this event gives rise to another trend, which incentivizes a different behavior, which sparks a new industry, which lobbies against this, which can cancel that, and so on endlessly.

To see how powerful these chain reactions can be, look at history, where it’s easy to skip the question, “And why is that?”

Take the question, “Why are student loans so high?”

Well, in part because millions of people ran to college when job prospects were dim in the mid-2000s.

Why were job prospects dim?

Well, there was a financial crisis in 2008.

Why?

Well, there was a housing bubble.

Why?

Well, interest rates were slashed in the early 2000s.

Why?

Well, 19 hijackers crashed planes on 9/11 that spooked the Fed into action to prevent a recession.

Why? Well …

You can keep asking, why? forever. And when do so you get these crazy connections, like a terrorist attack leading to student debt a decade later.

Every current event has parents, grandparents, great grandparents, siblings, and cousins. Ignoring that family tree can muddy your understanding of events, giving a false impression of why things happened, how long they might last, and under what circumstances they might happen again. Viewing events in isolation, without an appreciation for their deep roots, helps explain everything from why forecasting is hard to why politics is nasty.

Japan’s economy has been stagnant for 30 years because its demographics are terrible. Its demographics are terrible because it has a cultural preference for small families. That preference began in the late 1940s when, after losing its empire, its people nearly starved and froze to death each winter when the nation couldn’t support its existing population.

It was almost the opposite in America. The end of wartime production in 1945 scared policymakers, who feared a recession. So they did everything they could to make it easier for consumers to spend money, which boosted the economy, which inflated consumers’ social expectations, which led to a household debt boom that culminated with the 2008 crash.

No one looking at the last decade of economic performance blames Harry Truman. But you can draw a straight line from those decisions to what’s happening today.

6. “Ignoring the Possibility of Progress Is a Sure Method of Destroying Ourselves” – Rafaela von Bredow, Johann Grolle, and David Deutsch

DER SPIEGEL: Professor Deutsch, you believe that mankind, after billions and billions of years of absolute monotony in the universe, will now reshape it to their liking, that a new cosmological era is coming. Are you serious?

Deutsch: I am not the first to propose this idea. The Italian geologist Antonio Stoppani wrote in the 19th century that he had no hesitation in declaring man to be a new power in the universe, equivalent to the power of gravitation.

DER SPIEGEL: And fly to distant planets? Tap energy from black holes? Conquer entire galaxies?

Deutsch: I am not saying that we will necessarily do all this. I am only saying that, in principle, there is nothing to stop us. Only the laws of physics could prevent us. And we do not know a law of physics that forbids us, for example, from traveling to distant stars.

DER SPIEGEL: Theoretically, the colonization of the galaxy may be possible. But how would this work practically?

Deutsch: Human brains, assisted by our computers, can create the necessary knowledge for this – even though we do not yet know how.

DER SPIEGEL: Your late colleague Stephen Hawking did not have such high hopes for Homo sapiens. He thought we were “just a chemical scum on a moderate-sized planet, orbiting around a very average star in the outer suburb of one among a hundred billion galaxies.” Was Hawking wrong?

Deutsch: Well, it’s literally true. Just as it is true in a sense that the war in Ukraine was caused by atoms. It’s factually true, but it doesn’t explain anything. What we need to understand the world and our role in it are explanations, not empty statements.

DER SPIEGEL: Even among your fellow researchers, it might be hard to find many who grant us humans such a godlike role in the universe as you do.

Deutsch: Science is currently in a deplorable state. I’m reluctant to diss my colleagues, but, unfortunately, there’s a sort of cult of the expert. Accordingly, many researchers remain narrowly focused on their particular field, and even within that they are focused on creating usefulness rather than finding explanations. This is a terrible mistake.

DER SPIEGEL: What is so terrible about useful science?

Deutsch: All usefulness, every prediction, comes from understanding. However, if you no longer strive for fundamental explanations, but believe that it is sufficient to generate something useful, then you will merely move incrementally from one decimal place to the next, and even then, only in areas that are already well studied. This tendency has dramatically slowed down progress.

DER SPIEGEL: There are photos of a black hole, we can genetically modify people and develop a vaccine against a new pathogen within months. All this is not progress?

Deutsch: Yes, it is, but it is going slower than it could.

DER SPIEGEL: Biologist Richard Dawkins believes that this is perhaps because our brains are insufficient to comprehend the increasingly complex world. After all, it evolved to deal with problems on the African savanna. Now, however, we have to deal with stars, quantum and nuclear reactions.

Deutsch: Dawkins overlooks the fact that there is basically only one kind of computer. Whether it’s your laptop, or a supercomputer for modeling the climate, any computer can run the same computations. And our brain is nothing more than a universal computer. Its hardware can run any program, and we can use extra memory in our computers if necessary; therefore, it can run any explanation. There is no such thing as a computer that’s suitable for understanding the savanna, but not the sky. We couldn’t build one if we tried. It violates the laws of physics.

7. DALL•E 2 – Sam Altman

1) This is another example of what I think is going to be a new computer interface trend: you say what you want in natural language or with contextual clues, and the computer does it. We offer this for code and now image generation; both of these will get a lot better. But the same trend will happen in new ways until eventually it works for complex tasks—we can imagine an “AI office worker” that takes requests in natural language like a human does…

…3) Copilot is a tool that helps coders be more productive, but still is very far from being able to create a full program. DALL•E 2 is a tool that will help artists and illustrators be more creative, but it can also create a “complete work”. This may be an early example of the impact AI on labor markets. Although I firmly believe AI will create lots of new jobs, and make many existing jobs much better by doing the boring bits well, I think it’s important to be honest that it’s increasingly going to make some jobs not very relevant (like technology frequently does).

4) It’s a reminder that predictions about AI are very difficult to make. A decade ago, the conventional wisdom was that AI would first impact physical labor, and then cognitive labor, and then maybe someday it could do creative work. It now looks like it’s going to go in the opposite order.

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